{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# アイスクリーム統計学 + Python (第７章〜第９章)\n", "\n", "[アイスクリーム統計学にようこそ！](http://kogolab.chillout.jp/elearn/icecream/index.html)\n", "のページを読み進めながら、以下の課題を Python を使って解いてください。\n", "\n", "* [アイスクリーム統計学・第１章〜第３章（散布図と相関、相関係数、無相関検定）](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E3%82%A2%E3%82%A4%E3%82%B9%E3%82%AF%E3%83%AA%E3%83%BC%E3%83%A0%E7%B5%B1%E8%A8%88%E5%AD%A6%E3%83%BB%E7%AC%AC%EF%BC%91%E7%AB%A0%E3%80%9C%E7%AC%AC%EF%BC%93%E7%AB%A0.ipynb)\n", "* [アイスクリーム統計学・第４章〜第６章（単回帰、偏相関、重回帰）](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E3%82%A2%E3%82%A4%E3%82%B9%E3%82%AF%E3%83%AA%E3%83%BC%E3%83%A0%E7%B5%B1%E8%A8%88%E5%AD%A6%E3%83%BB%E7%AC%AC%EF%BC%94%E7%AB%A0%E3%80%9C%E7%AC%AC%EF%BC%96%E7%AB%A0.ipynb)\n", "* [アイスクリーム統計学・第７章〜第９章（相関行列、因子分析1、因子分析2）](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E3%82%A2%E3%82%A4%E3%82%B9%E3%82%AF%E3%83%AA%E3%83%BC%E3%83%A0%E7%B5%B1%E8%A8%88%E5%AD%A6%E3%83%BB%E7%AC%AC%EF%BC%97%E7%AB%A0%E3%80%9C%E7%AC%AC%EF%BC%99%E7%AB%A0.ipynb)\n", "* [アイスクリーム統計学・第１０章（主成分分析とクラスター分析）](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E3%82%A2%E3%82%A4%E3%82%B9%E3%82%AF%E3%83%AA%E3%83%BC%E3%83%A0%E7%B5%B1%E8%A8%88%E5%AD%A6%E3%83%BB%E7%AC%AC%EF%BC%91%EF%BC%90%E7%AB%A0.ipynb)\n", "* [ハンバーガー統計学・第１章〜第２章（平均と分散、信頼区間）](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E3%83%8F%E3%83%B3%E3%83%8F%E3%82%99%E3%83%BC%E3%82%AB%E3%82%99%E3%83%BC%E7%B5%B1%E8%A8%88%E5%AD%A6%E3%83%BB%E7%AC%AC%EF%BC%91%E7%AB%A0%E3%80%9C%E7%AC%AC%EF%BC%92%E7%AB%A0.ipynb)\n", "* [ハンバーガー統計学・第３章〜第５章（カイ２乗検定、t検定 （対応なし・対応あり））](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E3%83%8F%E3%83%B3%E3%83%8F%E3%82%99%E3%83%BC%E3%82%AB%E3%82%99%E3%83%BC%E7%B5%B1%E8%A8%88%E5%AD%A6%E3%83%BB%E7%AC%AC%EF%BC%93%E7%AB%A0%E3%80%9C%E7%AC%AC%EF%BC%95%E7%AB%A0.ipynb)\n", "* [ハンバーガー統計学・第６章〜第７章（分散分析 (１要因、２要因) ）](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E3%83%8F%E3%83%B3%E3%83%8F%E3%82%99%E3%83%BC%E3%82%AB%E3%82%99%E3%83%BC%E7%B5%B1%E8%A8%88%E5%AD%A6%E3%83%BB%E7%AC%AC%EF%BC%96%E7%AB%A0%E3%80%9C%E7%AC%AC%EF%BC%97%E7%AB%A0.ipynb)\n", "* [ハンバーガー統計学・第８章 (分布と仮説検定)](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E3%83%8F%E3%83%B3%E3%83%8F%E3%82%99%E3%83%BC%E3%82%AB%E3%82%99%E3%83%BC%E7%B5%B1%E8%A8%88%E5%AD%A6%E3%83%BB%E7%AC%AC%EF%BC%98%E7%AB%A0.ipynb)\n", "\n", "### Jupyter Notebook (IPython Notebook) とは\n", "* Python という名のプログラミング言語が使えるプログラミング環境。計算コードと計算結果を同じ場所に時系列で保存できるので、実験系における実験ノートのように、いつどんな処理を行って何を得たのか記録して再現するのに便利。\n", "* [当学演習室での Jupyter Notebook の起動方法](https://raw.githubusercontent.com/maskot1977/-/master/%E6%BC%94%E7%BF%92%E5%AE%A4.txt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7.　[相関行列](http://kogolab.chillout.jp/elearn/icecream/chap7/sec0.html)\n", "本文を読んで概念を学んでください。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.1　[多変量データ](http://kogolab.chillout.jp/elearn/icecream/chap7/sec1.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "本文を読んで概念を学んでください。また、 [調査票](https://github.com/maskot1977/ipython_notebook/blob/master/toydata/chosahyo.pdf)を読んで、アンケート内容を把握してください。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.2　[変数をまとめる](http://kogolab.chillout.jp/elearn/icecream/chap7/sec2.html)\n", "ここでは、人を「サンプル」と見なし、バニラ、ストロベリーなどの味の好みを「変数」と見なします。（逆に、バニラ、ストロベリーなどのアイスを「サンプル」と見なし、人の好みを「変数」と見なすこともできます。）" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__課題7.2__：[アイスクリームアンケート調査結果](https://raw.githubusercontent.com/maskot1977/ipython_notebook/master/toydata/icecream_chosa.txt)をダウンロードして、次のコードを実行してください。" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# URL によるリソースへのアクセスを提供するライブラリをインポートする。\n", "import urllib # Python 2 の場合\n", "# import urllib.request # Python 3 の場合" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ウェブ上のリソースを指定する\n", "url = 'https://raw.githubusercontent.com/maskot1977/ipython_notebook/master/toydata/icecream_chosa.txt'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "('icecream_chosa.txt', )" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 指定したURLからリソースをダウンロードし、名前をつける。\n", "urllib.urlretrieve(url, 'icecream_chosa.txt') # Python 2 の場合\n", "# urllib.request.urlretrieve(url, 'icecream_chosa.txt') # Python 3 の場合" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ID gender age birth_order frequency vanilla strawberry milk_tea macadamia_nuts cookie chocolate melon coffee almond rum_raisin mint banana caramel walnut cassis chocolate_chips orange green_tea marron chocolate_mint adzuki-bean \r\n", "1 2 20 2 5 7 7 8 3 9 9 6 8 3 2 1 7 9 3 3 9 7 8 8 1 7 \r\n", "2 2 21 2 1 7 8 9 9 9 9 2 7 7 9 5 4 7 8 9 9 4 7 7 8 9 \r\n", "3 2 21 3 2 7 4 3 3 6 4 7 7 6 3 3 4 6 3 3 6 7 3 7 3 3 \r\n", "4 2 21 4 3 9 6 6 5 8 6 8 9 6 5 4 4 9 5 4 8 9 1 2 1 3 \r\n", "5 2 21 4 2 9 5 7 5 6 8 4 4 4 6 9 6 6 6 5 9 6 6 6 9 1 \r\n", "6 2 21 2 5 5 7 5 5 5 7 5 8 5 8 9 5 9 5 8 8 9 9 5 9 1 \r\n", "7 2 21 2 2 9 7 6 3 7 9 4 6 5 2 2 6 7 5 5 9 9 3 5 2 3 \r\n", "8 2 20 2 4 7 7 6 8 7 6 5 8 7 5 6 5 6 7 9 5 6 8 5 6 8 \r\n", "9 2 21 2 4 7 7 4 8 7 7 4 7 6 3 5 5 7 7 6 8 6 3 3 7 3 \r\n" ] } ], "source": [ "#最初の数レコードだけ確認する\n", "!head icecream_chosa.txt" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# バニラ、ストロベリーなどの変数間の関係を見るための「行列」 matrix に格納する。\n", "# Pythonにおける「長さnのリスト（n個の要素が入ったリスト）」は、数学における「n次元の列ベクトル」と同じ意味。\n", "# Pythonにおける「リストのリスト（長さnのリストがm個入ったもの）」は、数学における「m行n列の行列」と同じ意味。\n", "# 変数の名前は val_names というリストに入れる\n", "matrix = []\n", "val_names = []\n", "for i, line in enumerate(open(\"icecream_chosa.txt\")):\n", " if i == 0:\n", " for j, val in enumerate(line.split()):\n", " val_names.append(val)\n", " matrix.append([])\n", " else:\n", " for j, val in enumerate(line.split()):\n", " matrix[j].append(float(val))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd # データフレームワーク処理のライブラリをインポート" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ID1.02.03.04.05.06.07.08.09.010.0...71.072.073.074.075.076.077.078.079.080.0
gender2.02.02.02.02.02.02.02.02.02.0...1.01.01.01.01.01.01.01.01.01.0
age20.021.021.021.021.021.021.020.021.022.0...22.024.020.020.021.020.020.023.023.022.0
birth_order2.02.03.04.04.02.02.02.02.04.0...4.01.02.02.02.02.03.01.04.02.0
frequency5.01.02.03.02.05.02.04.04.04.0...4.02.02.05.06.04.06.05.07.02.0
vanilla7.07.07.09.09.05.09.07.07.05.0...7.06.06.08.04.05.07.04.09.08.0
strawberry7.08.04.06.05.07.07.07.07.05.0...7.07.06.08.06.06.05.08.07.05.0
milk_tea8.09.03.06.07.05.06.06.04.09.0...6.05.05.04.02.04.06.08.07.05.0
macadamia_nuts3.09.03.05.05.05.03.08.08.05.0...6.06.08.08.03.09.06.09.05.02.0
cookie9.09.06.08.06.05.07.07.07.08.0...8.04.08.09.06.06.08.09.04.05.0
chocolate9.09.04.06.08.07.09.06.07.08.0...9.08.07.07.06.07.09.09.06.04.0
melon6.02.07.08.04.05.04.05.04.05.0...6.08.03.03.08.04.04.04.07.07.0
coffee8.07.07.09.04.08.06.08.07.06.0...5.07.06.06.05.03.04.08.07.01.0
almond3.07.06.06.04.05.05.07.06.03.0...5.06.07.06.04.06.04.09.04.05.0
rum_raisin2.09.03.05.06.08.02.05.03.03.0...5.08.07.03.02.05.05.01.05.05.0
mint1.05.03.04.09.09.02.06.05.03.0...4.04.08.08.02.04.04.09.08.03.0
banana7.04.04.04.06.05.06.05.05.08.0...5.04.06.05.03.05.03.06.09.04.0
caramel9.07.06.09.06.09.07.06.07.07.0...7.02.07.04.02.05.09.08.07.05.0
walnut3.08.03.05.06.05.05.07.07.02.0...5.06.08.08.03.05.05.09.04.05.0
cassis3.09.03.04.05.08.05.09.06.08.0...5.08.07.08.02.05.05.05.05.05.0
chocolate_chips9.09.06.08.09.08.09.05.08.08.0...9.04.08.09.06.07.09.09.08.05.0
orange7.04.07.09.06.09.09.06.06.06.0...7.07.05.08.08.07.07.09.08.07.0
green_tea8.07.03.01.06.09.03.08.03.06.0...3.08.08.09.01.04.06.09.05.01.0
marron8.07.07.02.06.05.05.05.03.02.0...7.03.08.06.01.04.05.04.05.01.0
chocolate_mint1.08.03.01.09.09.02.06.07.01.0...6.02.08.09.02.04.06.09.08.05.0
adzuki-bean7.09.03.03.01.01.03.08.03.01.0...8.06.07.04.02.04.07.09.04.05.0
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できあがったMatrixの中身を確認してください。\n", "pd.DataFrame(matrix, index=val_names)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# バニラ、ストロベリーなどアイスクリームの種類に関するデータは５行目以降なので、Matrixを作り直してください。\n", "matrix = []\n", "val_names = []\n", "sta = 5\n", "for i, line in enumerate(open(\"icecream_chosa.txt\")):" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# できあがったMatrixの中身を確認してください。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.3　[相関行列を計算する(1)](http://kogolab.chillout.jp/elearn/icecream/chap7/sec3.html)\n", "本文を読んで概念を学んでください。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.4　[相関行列を計算する(2)](http://kogolab.chillout.jp/elearn/icecream/chap7/sec4.html)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 平均値を求める関数\n", "def average(list):" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 分散を求める関数\n", "def variance(list):" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 標準偏差を求める関数\n", "def standard_deviation(list):" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 共分散を求める関数\n", "def covariance(list1, list2):" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 相関係数を求める関数\n", "def correlation(list1, list2):" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 相関行列を作る関数\n", "def correlation_matrix(matrix):" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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0.295318 0.209069 \n", "6 0.193373 -0.018380 -0.138505 ... 0.241593 0.133873 0.032196 \n", "7 1.000000 0.259505 0.009779 ... 0.288617 0.283974 0.100008 \n", "8 0.259505 1.000000 0.141709 ... 0.086892 0.308005 0.546367 \n", "9 0.009779 0.141709 1.000000 ... -0.059180 0.047346 0.081568 \n", "10 0.081165 0.177390 0.126706 ... 0.111364 0.130338 0.145211 \n", "11 0.288617 0.086892 -0.059180 ... 1.000000 0.433279 0.128464 \n", "12 0.283974 0.308005 0.047346 ... 0.433279 1.000000 0.289593 \n", "13 0.100008 0.546367 0.081568 ... 0.128464 0.289593 1.000000 \n", "14 0.312881 0.154778 0.100993 ... 0.241141 0.213386 0.351503 \n", "15 0.242711 0.277636 -0.040930 ... 0.325065 0.342706 0.128733 \n", "16 0.213161 0.036828 -0.190443 ... -0.017298 0.176749 0.080530 \n", "17 0.237421 0.078271 0.219083 ... 0.089488 0.048373 0.282190 \n", "18 0.157815 0.074451 0.122659 ... 0.383984 0.458127 0.269376 \n", "19 0.001101 0.155004 0.147367 ... 0.072743 0.137117 0.252747 \n", "20 0.057754 0.217668 0.012173 ... 0.198064 0.339599 0.330305 \n", "\n", " 14 15 16 17 18 19 20 \n", "0 -0.157352 0.021834 -0.010688 0.015440 0.132379 -0.211786 0.049856 \n", "1 0.149930 0.155422 0.170910 0.169990 0.124279 -0.013666 0.213412 \n", "2 0.334752 0.106697 0.090938 0.285318 0.285782 -0.019108 0.218824 \n", "3 0.302949 0.079059 -0.056018 0.310694 0.291890 0.114404 0.311802 \n", "4 0.094633 0.442330 -0.074943 0.016897 0.246303 -0.156397 0.248015 \n", "5 0.099631 0.661099 0.123757 0.047984 0.109969 0.191060 0.209861 \n", "6 0.058476 0.106230 0.197470 -0.158637 -0.079930 -0.109765 0.030297 \n", "7 0.312881 0.242711 0.213161 0.237421 0.157815 0.001101 0.057754 \n", "8 0.154778 0.277636 0.036828 0.078271 0.074451 0.155004 0.217668 \n", "9 0.100993 -0.040930 -0.190443 0.219083 0.122659 0.147367 0.012173 \n", "10 0.123642 0.160271 0.020442 0.205284 0.076743 0.833957 -0.095770 \n", "11 0.241141 0.325065 -0.017298 0.089488 0.383984 0.072743 0.198064 \n", "12 0.213386 0.342706 0.176749 0.048373 0.458127 0.137117 0.339599 \n", "13 0.351503 0.128733 0.080530 0.282190 0.269376 0.252747 0.330305 \n", "14 1.000000 0.123230 0.115750 0.250157 0.204307 0.177092 0.218333 \n", "15 0.123230 1.000000 0.142654 -0.020819 0.097843 0.264442 0.118129 \n", "16 0.115750 0.142654 1.000000 0.070940 -0.053342 0.017846 -0.039698 \n", "17 0.250157 -0.020819 0.070940 1.000000 0.385409 0.214660 0.348820 \n", "18 0.204307 0.097843 -0.053342 0.385409 1.000000 0.157897 0.509220 \n", "19 0.177092 0.264442 0.017846 0.214660 0.157897 1.000000 0.023781 \n", "20 0.218333 0.118129 -0.039698 0.348820 0.509220 0.023781 1.000000 \n", "\n", "[21 rows x 21 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 相関行列を計算し表示する\n", "cormat = correlation_matrix(matrix)\n", "pd.DataFrame(cormat)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 図やグラフを図示するためのライブラリをインポートする。\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 得られた相関行列をカラーマップで表示する。\n", "import matplotlib.ticker as ticker\n", "plt.pcolor(cormat, cmap=plt.cm.Blues)\n", "plt.colorbar()\n", "plt.gca().get_xaxis().set_major_locator(ticker.MaxNLocator(integer=True))\n", "plt.gca().get_yaxis().set_major_locator(ticker.MaxNLocator(integer=True))\n", "plt.xlim([0, len(cormat)])\n", "plt.ylim([0, len(cormat)])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.6　[通過テスト](http://kogolab.chillout.jp/elearn/icecream/chap7/sec6.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__課題7.6__：[アイスクリームアンケート調査結果](https://raw.githubusercontent.com/maskot1977/ipython_notebook/master/toydata/icecream_chosa.txt)のデータを用い、以下の問いに答えてください。\n", "* 21種類のアイスクリームの好みのデータについて、すべての組み合わせの相関係数を求めた上で、正の相関が高いもの（ここでは+0.4以上）の組み合わせをリストしてください。\n", "* 同様に、相関行列の中で、負の相関が高いもの（ここでは-0.2以下）の組み合わせをリストしてください。\n", "* 以上の結果を踏まえて、アイスクリームの好みについての分類をしたときに、言えることを書いてください。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 8.　[因子分析(1)](http://kogolab.chillout.jp/elearn/icecream/chap8/sec0.html)\n", "本文を読んで概念を学んでください。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 8.1　[因子分析の考え方](http://kogolab.chillout.jp/elearn/icecream/chap8/sec1.html)\n", "本文を読んで概念を学んでください。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 8.2　[固有値](http://kogolab.chillout.jp/elearn/icecream/chap8/sec2.html)\n", "本文を読んで概念を学んでください。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 8.3　[因子負荷](http://kogolab.chillout.jp/elearn/icecream/chap8/sec3.html)\n", "本文を読んで概念を学んでください。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 8.4　[回転](http://kogolab.chillout.jp/elearn/icecream/chap8/sec4.html)\n", "本文を読んで概念を学んでください。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 8.5　[通過テスト](http://kogolab.chillout.jp/elearn/icecream/chap8/sec6.html)\n", "Pythonでは、因子分析を行うためのライブラリが使えます。\n", "\n", "__課題8.5__：因子分析を行う前に、リストの正規化をする（Z値に変換する）関数を作成してください。" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# リストの正規化をする（Z値に変換する）関数\n", "def normalize(data):" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# リストのリストの正規化をする（Z値に変換する）関数\n", "def normalize2(data):\n", " list = []\n", " for x in data:\n", " list.append(normalize(x))\n", " return list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__課題8.6__：因子分析を行う以下のコードを実行してください。" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 図やグラフを図示するためのライブラリをインポートする。\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 機械学習のライブラリから、因子分析のプログラムをインポート\n", "from sklearn.decomposition import FactorAnalysis" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 因子分析の実行\n", "fa = FactorAnalysis()\n", "factors = fa.fit_transform(normalize2(matrix))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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0123456789...11121314151617181920
vanilla-1.268569-0.0104242.318349-1.735447-1.382064-0.5705990.0433041.511889-0.7918650.436107...-1.0552571.2538820.161477-0.764865-0.873683-0.2200230.6115760.0830700.2366530.0
strawberry-0.3621260.5141870.8287580.426349-0.0253112.404494-0.876892-1.471251-1.200159-1.630133...-0.1410870.7663271.6676940.562325-0.3541020.694906-0.089582-0.9325070.1667170.0
milk_tea-0.779537-0.4687270.4443151.0644070.688036-0.4970591.139735-0.838651-2.017942-0.732853...0.6204200.552054-1.5357570.319772-0.017558-1.123373-1.6926770.4121580.1033140.0
macadamia_nuts0.311445-1.707769-0.8172740.556034-0.726757-0.2066910.1280000.437522-0.584647-0.277903...0.306305-0.1702831.760713-0.0919571.4724631.2855291.4780710.8768220.7106900.0
cookie-1.458831-0.541890-1.499313-1.012749-0.970025-0.2178020.0463120.0420970.123070-0.748735...-0.932264-1.312443-0.6158762.710530-0.165078-0.8906600.740134-0.963135-0.3232010.0
chocolate-0.0240041.132431-1.190455-1.1166730.047021-0.392829-1.139540-1.0871550.3343340.564087...1.1187160.2500170.734378-1.6033840.098381-1.8078910.297841-1.7761100.1919920.0
melon-0.7565431.8077380.3289621.286306-0.1799941.7272510.3114771.1556712.276051-0.372793...0.155506-0.528329-0.4718450.102185-0.714128-0.0718460.1513550.8553060.3505340.0
coffee-0.6489790.384299-0.0615861.070261-0.617858-2.3393951.278930-0.3905191.665691-0.372049...0.2858451.2985041.1754830.370414-0.0713591.163895-1.140516-1.1290700.0177620.0
almond0.550480-0.495058-1.6840240.228919-1.3999480.6560780.5926460.1895910.259340-1.010834...-0.3281611.373537-0.468381-1.2938560.259451-1.615691-0.0304361.5223420.7248860.0
rum_raisin0.758196-0.8749341.325704-0.696747-1.6947271.3157421.577272-1.1633270.6063881.541298...1.407346-1.161449-0.5046670.0116470.9024070.098465-0.495305-0.498085-0.3814380.0
mint2.4755830.8595360.378847-0.3197730.466233-0.3209640.7731510.393056-0.076465-0.841273...-0.4836450.6950110.2452910.6720470.181762-0.5478090.8310970.141547-2.9339250.0
banana-0.7869820.541323-0.029725-0.1679101.8845160.1445921.804551-0.182355-0.200982-0.191326...-1.943758-1.264642-0.229517-1.7010191.4345170.4399410.682724-0.7633810.3197200.0
caramel-0.4162610.307764-0.745029-0.0239851.3698550.1199221.0096360.779210-0.9502730.988364...2.3964360.570968-0.3234040.456950-1.4326790.6291281.8804400.0442790.2638400.0
walnut0.654143-1.271467-1.0700620.780755-0.3849900.521873-0.5844561.621193-0.5677370.168249...-0.460775-0.857318-0.409739-1.346983-1.7247831.025217-1.389702-1.592031-1.0140180.0
cassis0.175942-0.4372880.0278661.7693190.3989310.032984-0.568923-1.0766110.0422242.937834...-1.8799920.4928590.5720920.716653-0.667108-0.9318650.4381110.482704-0.0063780.0
chocolate_chips-0.2263681.397066-1.208100-1.645448-0.312818-0.345273-0.420425-1.341062-0.4838760.561314...-0.389309-0.455137-0.152339-0.173965-0.5943931.955030-1.1345012.207914-0.5513120.0
orange-0.1630431.6038670.6016731.508254-0.964408-1.057155-1.7113241.006225-1.2374340.208638...0.741975-1.285698-0.3760030.0062711.785554-0.1988880.0466660.183563-0.2787200.0
green_tea0.571728-1.1755871.3087130.3064760.342418-1.306810-1.144870-1.5853951.014943-1.466980...0.223698-1.185572-1.006683-0.777827-1.4592620.1238851.3821540.5422760.3665920.0
marron-0.417869-1.0754060.532979-0.9144451.786432-0.044534-0.3744871.0700830.676474-0.098205...0.717336-1.0569362.0977050.3161350.056157-1.317938-1.6521461.211806-0.2070680.0
chocolate_mint2.4797260.6154350.196853-0.8235840.517045-0.232910-0.1767520.699204-0.0978360.052221...-0.5260640.089772-0.5465671.2508710.0762600.314771-0.771863-0.6466782.9357540.0
adzuki-bean-0.668130-1.1050960.012550-0.5403191.1584160.609084-1.7073440.2305841.2107010.284971...0.1667291.934876-1.7740550.2580551.8071810.995216-0.143440-0.262790-0.6923930.0
\n", "

21 rows × 21 columns

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" ], "text/plain": [ " 0 1 2 3 4 5 \\\n", "vanilla -1.268569 -0.010424 2.318349 -1.735447 -1.382064 -0.570599 \n", "strawberry -0.362126 0.514187 0.828758 0.426349 -0.025311 2.404494 \n", "milk_tea -0.779537 -0.468727 0.444315 1.064407 0.688036 -0.497059 \n", "macadamia_nuts 0.311445 -1.707769 -0.817274 0.556034 -0.726757 -0.206691 \n", "cookie -1.458831 -0.541890 -1.499313 -1.012749 -0.970025 -0.217802 \n", "chocolate -0.024004 1.132431 -1.190455 -1.116673 0.047021 -0.392829 \n", "melon -0.756543 1.807738 0.328962 1.286306 -0.179994 1.727251 \n", "coffee -0.648979 0.384299 -0.061586 1.070261 -0.617858 -2.339395 \n", "almond 0.550480 -0.495058 -1.684024 0.228919 -1.399948 0.656078 \n", "rum_raisin 0.758196 -0.874934 1.325704 -0.696747 -1.694727 1.315742 \n", "mint 2.475583 0.859536 0.378847 -0.319773 0.466233 -0.320964 \n", "banana -0.786982 0.541323 -0.029725 -0.167910 1.884516 0.144592 \n", "caramel -0.416261 0.307764 -0.745029 -0.023985 1.369855 0.119922 \n", "walnut 0.654143 -1.271467 -1.070062 0.780755 -0.384990 0.521873 \n", "cassis 0.175942 -0.437288 0.027866 1.769319 0.398931 0.032984 \n", "chocolate_chips -0.226368 1.397066 -1.208100 -1.645448 -0.312818 -0.345273 \n", "orange -0.163043 1.603867 0.601673 1.508254 -0.964408 -1.057155 \n", "green_tea 0.571728 -1.175587 1.308713 0.306476 0.342418 -1.306810 \n", "marron -0.417869 -1.075406 0.532979 -0.914445 1.786432 -0.044534 \n", "chocolate_mint 2.479726 0.615435 0.196853 -0.823584 0.517045 -0.232910 \n", "adzuki-bean -0.668130 -1.105096 0.012550 -0.540319 1.158416 0.609084 \n", "\n", " 6 7 8 9 ... 11 \\\n", "vanilla 0.043304 1.511889 -0.791865 0.436107 ... -1.055257 \n", "strawberry -0.876892 -1.471251 -1.200159 -1.630133 ... -0.141087 \n", "milk_tea 1.139735 -0.838651 -2.017942 -0.732853 ... 0.620420 \n", "macadamia_nuts 0.128000 0.437522 -0.584647 -0.277903 ... 0.306305 \n", "cookie 0.046312 0.042097 0.123070 -0.748735 ... -0.932264 \n", "chocolate -1.139540 -1.087155 0.334334 0.564087 ... 1.118716 \n", "melon 0.311477 1.155671 2.276051 -0.372793 ... 0.155506 \n", "coffee 1.278930 -0.390519 1.665691 -0.372049 ... 0.285845 \n", "almond 0.592646 0.189591 0.259340 -1.010834 ... -0.328161 \n", "rum_raisin 1.577272 -1.163327 0.606388 1.541298 ... 1.407346 \n", "mint 0.773151 0.393056 -0.076465 -0.841273 ... -0.483645 \n", "banana 1.804551 -0.182355 -0.200982 -0.191326 ... -1.943758 \n", "caramel 1.009636 0.779210 -0.950273 0.988364 ... 2.396436 \n", "walnut -0.584456 1.621193 -0.567737 0.168249 ... -0.460775 \n", "cassis -0.568923 -1.076611 0.042224 2.937834 ... -1.879992 \n", "chocolate_chips -0.420425 -1.341062 -0.483876 0.561314 ... -0.389309 \n", "orange -1.711324 1.006225 -1.237434 0.208638 ... 0.741975 \n", "green_tea -1.144870 -1.585395 1.014943 -1.466980 ... 0.223698 \n", "marron -0.374487 1.070083 0.676474 -0.098205 ... 0.717336 \n", "chocolate_mint -0.176752 0.699204 -0.097836 0.052221 ... -0.526064 \n", "adzuki-bean -1.707344 0.230584 1.210701 0.284971 ... 0.166729 \n", "\n", " 12 13 14 15 16 17 \\\n", "vanilla 1.253882 0.161477 -0.764865 -0.873683 -0.220023 0.611576 \n", "strawberry 0.766327 1.667694 0.562325 -0.354102 0.694906 -0.089582 \n", "milk_tea 0.552054 -1.535757 0.319772 -0.017558 -1.123373 -1.692677 \n", "macadamia_nuts -0.170283 1.760713 -0.091957 1.472463 1.285529 1.478071 \n", "cookie -1.312443 -0.615876 2.710530 -0.165078 -0.890660 0.740134 \n", "chocolate 0.250017 0.734378 -1.603384 0.098381 -1.807891 0.297841 \n", "melon -0.528329 -0.471845 0.102185 -0.714128 -0.071846 0.151355 \n", "coffee 1.298504 1.175483 0.370414 -0.071359 1.163895 -1.140516 \n", "almond 1.373537 -0.468381 -1.293856 0.259451 -1.615691 -0.030436 \n", "rum_raisin -1.161449 -0.504667 0.011647 0.902407 0.098465 -0.495305 \n", "mint 0.695011 0.245291 0.672047 0.181762 -0.547809 0.831097 \n", "banana -1.264642 -0.229517 -1.701019 1.434517 0.439941 0.682724 \n", "caramel 0.570968 -0.323404 0.456950 -1.432679 0.629128 1.880440 \n", "walnut -0.857318 -0.409739 -1.346983 -1.724783 1.025217 -1.389702 \n", "cassis 0.492859 0.572092 0.716653 -0.667108 -0.931865 0.438111 \n", "chocolate_chips -0.455137 -0.152339 -0.173965 -0.594393 1.955030 -1.134501 \n", "orange -1.285698 -0.376003 0.006271 1.785554 -0.198888 0.046666 \n", "green_tea -1.185572 -1.006683 -0.777827 -1.459262 0.123885 1.382154 \n", "marron -1.056936 2.097705 0.316135 0.056157 -1.317938 -1.652146 \n", "chocolate_mint 0.089772 -0.546567 1.250871 0.076260 0.314771 -0.771863 \n", "adzuki-bean 1.934876 -1.774055 0.258055 1.807181 0.995216 -0.143440 \n", "\n", " 18 19 20 \n", "vanilla 0.083070 0.236653 0.0 \n", "strawberry -0.932507 0.166717 0.0 \n", "milk_tea 0.412158 0.103314 0.0 \n", "macadamia_nuts 0.876822 0.710690 0.0 \n", "cookie -0.963135 -0.323201 0.0 \n", "chocolate -1.776110 0.191992 0.0 \n", "melon 0.855306 0.350534 0.0 \n", "coffee -1.129070 0.017762 0.0 \n", "almond 1.522342 0.724886 0.0 \n", "rum_raisin -0.498085 -0.381438 0.0 \n", "mint 0.141547 -2.933925 0.0 \n", "banana -0.763381 0.319720 0.0 \n", "caramel 0.044279 0.263840 0.0 \n", "walnut -1.592031 -1.014018 0.0 \n", "cassis 0.482704 -0.006378 0.0 \n", "chocolate_chips 2.207914 -0.551312 0.0 \n", "orange 0.183563 -0.278720 0.0 \n", "green_tea 0.542276 0.366592 0.0 \n", "marron 1.211806 -0.207068 0.0 \n", "chocolate_mint -0.646678 2.935754 0.0 \n", "adzuki-bean -0.262790 -0.692393 0.0 \n", "\n", "[21 rows x 21 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 左から順に第一因子、第二因子、、、\n", "pd.DataFrame(factors, index=val_names)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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mbsLn68/y5a+SlJQU0mMtVY007BTj8FOMI0NxDr9oSyqC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\n7rjjDn71q18BBcWX9erVIykpCYBTp07Rq1cvbrnlFiZNmsS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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 第一因子と第二因子でプロットする\n", "plt.figure(figsize=(8, 8))\n", "for x, y, name in zip(factors[:, 0], factors[:, 1], val_names):\n", " plt.text(x, y, name, alpha=0.8, size=10)\n", "plt.scatter(factors[:, 0], factors[:, 1], alpha=0.8)\n", "plt.title(\"Factor Analysis\")\n", "plt.xlabel(\"The first factor score\")\n", "plt.ylabel(\"The second factor score\")\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__課題8.7__：上と同様にして、第三因子と第四因子でプロットしてください。また、以上の結果から、アイスクリームの味の好みについて何が言えそうか考察してください。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 9.　[因子分析(2)](http://kogolab.chillout.jp/elearn/icecream/chap9/sec0.html)\n", "本文を読んで概念を学んでください。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 9.1　[単純構造](http://kogolab.chillout.jp/elearn/icecream/chap9/sec1.html)\n", "本文を読んで概念を学んでください。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 9.2　[因子得点](http://kogolab.chillout.jp/elearn/icecream/chap9/sec2.html)\n", "本文を読んで概念を学んでください。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 9.3　[通過テスト](http://kogolab.chillout.jp/elearn/icecream/chap9/sec3.html)\n", "今までは、アイスクリーム間の関係を解析しましたが、ここからは、人の間の関係を解析します。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__課題9.3__：[アイスクリームアンケート調査結果](https://raw.githubusercontent.com/maskot1977/ipython_notebook/master/toydata/icecream_chosa.txt)をダウンロードして、先ほどの matrix と縦横を入れ替えた matrixTを作成してください。" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 縦横入れ替えた matrixT を作成\n", "matrixT = []\n", "val_names = []\n", "ids = []\n", "sta = 5\n", "for i, line in enumerate(open(\"icecream_chosa.txt\")):\n", " if i == 0:\n", " for j, val in enumerate(line.split()):\n", " val_names.append(val)\n", " else:\n", " vec = []\n", " for j, val in enumerate(line.split()):\n", " if j == 0:\n", " ids.append(val)\n", " vec.append(float(val))\n", " matrixT.append(vec)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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IDgenderagebirth_orderfrequencyvanillastrawberrymilk_teamacadamia_nutscookie...bananacaramelwalnutcassischocolate_chipsorangegreen_teamarronchocolate_mintadzuki-bean
01.02.020.02.05.07.07.08.03.09.0...7.09.03.03.09.07.08.08.01.07.0
12.02.021.02.01.07.08.09.09.09.0...4.07.08.09.09.04.07.07.08.09.0
23.02.021.03.02.07.04.03.03.06.0...4.06.03.03.06.07.03.07.03.03.0
34.02.021.04.03.09.06.06.05.08.0...4.09.05.04.08.09.01.02.01.03.0
45.02.021.04.02.09.05.07.05.06.0...6.06.06.05.09.06.06.06.09.01.0
56.02.021.02.05.05.07.05.05.05.0...5.09.05.08.08.09.09.05.09.01.0
67.02.021.02.02.09.07.06.03.07.0...6.07.05.05.09.09.03.05.02.03.0
78.02.020.02.04.07.07.06.08.07.0...5.06.07.09.05.06.08.05.06.08.0
89.02.021.02.04.07.07.04.08.07.0...5.07.07.06.08.06.03.03.07.03.0
910.02.022.04.04.05.05.09.05.08.0...8.07.02.08.08.06.06.02.01.01.0
1011.02.022.02.03.08.01.08.09.07.0...4.09.07.06.06.07.08.06.04.05.0
1112.02.021.04.03.09.04.07.05.08.0...4.04.05.08.09.05.06.04.09.07.0
1213.02.021.04.04.07.07.06.08.08.0...5.09.07.05.09.07.07.06.08.08.0
1314.02.020.04.02.09.06.04.06.09.0...6.07.08.04.09.09.08.09.07.08.0
1415.02.020.02.04.08.07.05.05.05.0...4.04.06.05.08.06.09.04.09.05.0
1516.02.021.04.02.07.04.07.04.07.0...6.06.06.06.04.06.08.05.07.07.0
1617.02.020.02.02.06.04.02.02.07.0...4.06.06.05.07.05.09.06.09.06.0
1718.02.020.03.02.08.07.05.05.06.0...3.05.04.05.07.09.08.04.09.04.0
1819.02.021.04.06.05.09.08.05.07.0...6.06.07.07.08.07.09.05.08.05.0
1920.02.020.04.02.06.06.06.08.08.0...5.07.07.07.07.06.07.07.08.08.0
2021.02.021.04.03.09.09.06.06.08.0...7.06.05.09.09.09.09.07.04.08.0
2122.02.021.01.04.07.09.09.07.09.0...8.08.08.05.08.06.07.08.04.08.0
2223.02.021.02.04.08.08.09.08.08.0...5.08.08.04.09.08.09.05.05.08.0
2324.02.020.04.04.07.07.06.05.04.0...9.09.03.07.07.03.05.09.09.09.0
2425.02.020.01.03.07.06.07.09.09.0...6.08.08.05.08.04.08.09.03.09.0
2526.02.021.04.04.07.05.08.07.09.0...5.04.04.08.09.08.08.06.01.07.0
2627.02.020.02.05.07.09.07.07.08.0...2.08.08.09.08.09.06.03.01.07.0
2728.02.021.02.04.07.06.01.07.08.0...3.06.06.04.08.04.08.07.08.07.0
2829.02.021.03.04.06.07.07.08.08.0...5.09.08.07.09.07.07.05.05.03.0
2930.02.021.04.04.07.07.07.07.09.0...9.09.05.05.09.06.09.07.09.06.0
..................................................................
5051.01.019.02.06.07.08.06.03.05.0...4.05.02.03.08.05.08.03.08.02.0
5152.01.021.01.02.09.04.05.03.07.0...8.06.02.04.09.08.07.05.05.09.0
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" ], "text/plain": [ " ID gender age birth_order frequency vanilla strawberry milk_tea \\\n", "0 1.0 2.0 20.0 2.0 5.0 7.0 7.0 8.0 \n", "1 2.0 2.0 21.0 2.0 1.0 7.0 8.0 9.0 \n", "2 3.0 2.0 21.0 3.0 2.0 7.0 4.0 3.0 \n", "3 4.0 2.0 21.0 4.0 3.0 9.0 6.0 6.0 \n", "4 5.0 2.0 21.0 4.0 2.0 9.0 5.0 7.0 \n", "5 6.0 2.0 21.0 2.0 5.0 5.0 7.0 5.0 \n", "6 7.0 2.0 21.0 2.0 2.0 9.0 7.0 6.0 \n", "7 8.0 2.0 20.0 2.0 4.0 7.0 7.0 6.0 \n", "8 9.0 2.0 21.0 2.0 4.0 7.0 7.0 4.0 \n", "9 10.0 2.0 22.0 4.0 4.0 5.0 5.0 9.0 \n", "10 11.0 2.0 22.0 2.0 3.0 8.0 1.0 8.0 \n", "11 12.0 2.0 21.0 4.0 3.0 9.0 4.0 7.0 \n", "12 13.0 2.0 21.0 4.0 4.0 7.0 7.0 6.0 \n", "13 14.0 2.0 20.0 4.0 2.0 9.0 6.0 4.0 \n", "14 15.0 2.0 20.0 2.0 4.0 8.0 7.0 5.0 \n", "15 16.0 2.0 21.0 4.0 2.0 7.0 4.0 7.0 \n", "16 17.0 2.0 20.0 2.0 2.0 6.0 4.0 2.0 \n", "17 18.0 2.0 20.0 3.0 2.0 8.0 7.0 5.0 \n", "18 19.0 2.0 21.0 4.0 6.0 5.0 9.0 8.0 \n", "19 20.0 2.0 20.0 4.0 2.0 6.0 6.0 6.0 \n", "20 21.0 2.0 21.0 4.0 3.0 9.0 9.0 6.0 \n", "21 22.0 2.0 21.0 1.0 4.0 7.0 9.0 9.0 \n", "22 23.0 2.0 21.0 2.0 4.0 8.0 8.0 9.0 \n", "23 24.0 2.0 20.0 4.0 4.0 7.0 7.0 6.0 \n", "24 25.0 2.0 20.0 1.0 3.0 7.0 6.0 7.0 \n", "25 26.0 2.0 21.0 4.0 4.0 7.0 5.0 8.0 \n", "26 27.0 2.0 20.0 2.0 5.0 7.0 9.0 7.0 \n", "27 28.0 2.0 21.0 2.0 4.0 7.0 6.0 1.0 \n", "28 29.0 2.0 21.0 3.0 4.0 6.0 7.0 7.0 \n", "29 30.0 2.0 21.0 4.0 4.0 7.0 7.0 7.0 \n", ".. ... ... ... ... ... ... ... ... \n", "50 51.0 1.0 19.0 2.0 6.0 7.0 8.0 6.0 \n", "51 52.0 1.0 21.0 1.0 2.0 9.0 4.0 5.0 \n", "52 53.0 1.0 20.0 1.0 5.0 9.0 8.0 7.0 \n", "53 54.0 1.0 21.0 4.0 5.0 8.0 8.0 7.0 \n", "54 55.0 1.0 19.0 4.0 4.0 7.0 7.0 8.0 \n", "55 56.0 1.0 20.0 2.0 2.0 5.0 7.0 5.0 \n", "56 57.0 1.0 22.0 1.0 3.0 9.0 9.0 9.0 \n", "57 58.0 1.0 19.0 1.0 3.0 9.0 3.0 5.0 \n", "58 59.0 1.0 20.0 2.0 3.0 7.0 7.0 7.0 \n", "59 60.0 1.0 20.0 4.0 8.0 7.0 8.0 8.0 \n", "60 61.0 1.0 18.0 2.0 4.0 6.0 4.0 3.0 \n", "61 62.0 1.0 25.0 2.0 3.0 7.0 8.0 6.0 \n", "62 63.0 1.0 19.0 2.0 2.0 6.0 6.0 7.0 \n", "63 64.0 1.0 21.0 2.0 4.0 8.0 7.0 9.0 \n", "64 65.0 1.0 21.0 2.0 6.0 7.0 8.0 5.0 \n", "65 66.0 1.0 20.0 2.0 1.0 7.0 7.0 7.0 \n", "66 67.0 1.0 22.0 2.0 5.0 7.0 7.0 5.0 \n", "67 68.0 1.0 21.0 2.0 5.0 5.0 5.0 6.0 \n", "68 69.0 1.0 21.0 3.0 1.0 8.0 7.0 5.0 \n", "69 70.0 1.0 21.0 2.0 6.0 7.0 7.0 9.0 \n", "70 71.0 1.0 22.0 4.0 4.0 7.0 7.0 6.0 \n", "71 72.0 1.0 24.0 1.0 2.0 6.0 7.0 5.0 \n", "72 73.0 1.0 20.0 2.0 2.0 6.0 6.0 5.0 \n", "73 74.0 1.0 20.0 2.0 5.0 8.0 8.0 4.0 \n", "74 75.0 1.0 21.0 2.0 6.0 4.0 6.0 2.0 \n", "75 76.0 1.0 20.0 2.0 4.0 5.0 6.0 4.0 \n", "76 77.0 1.0 20.0 3.0 6.0 7.0 5.0 6.0 \n", "77 78.0 1.0 23.0 1.0 5.0 4.0 8.0 8.0 \n", "78 79.0 1.0 23.0 4.0 7.0 9.0 7.0 7.0 \n", "79 80.0 1.0 22.0 2.0 2.0 8.0 5.0 5.0 \n", "\n", " macadamia_nuts cookie ... banana caramel walnut cassis \\\n", "0 3.0 9.0 ... 7.0 9.0 3.0 3.0 \n", "1 9.0 9.0 ... 4.0 7.0 8.0 9.0 \n", "2 3.0 6.0 ... 4.0 6.0 3.0 3.0 \n", "3 5.0 8.0 ... 4.0 9.0 5.0 4.0 \n", "4 5.0 6.0 ... 6.0 6.0 6.0 5.0 \n", "5 5.0 5.0 ... 5.0 9.0 5.0 8.0 \n", "6 3.0 7.0 ... 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9.0 7.0 6.0 \n", "53 5.0 7.0 ... 5.0 4.0 4.0 4.0 \n", "54 7.0 6.0 ... 5.0 6.0 5.0 9.0 \n", "55 5.0 5.0 ... 4.0 6.0 5.0 5.0 \n", "56 9.0 9.0 ... 7.0 8.0 6.0 6.0 \n", "57 7.0 6.0 ... 5.0 1.0 6.0 3.0 \n", "58 4.0 6.0 ... 7.0 8.0 3.0 6.0 \n", "59 5.0 7.0 ... 5.0 6.0 4.0 5.0 \n", "60 5.0 9.0 ... 3.0 4.0 3.0 5.0 \n", "61 5.0 9.0 ... 7.0 5.0 5.0 5.0 \n", "62 7.0 7.0 ... 6.0 8.0 7.0 5.0 \n", "63 6.0 9.0 ... 8.0 4.0 5.0 9.0 \n", "64 4.0 6.0 ... 4.0 7.0 7.0 5.0 \n", "65 7.0 8.0 ... 7.0 4.0 6.0 5.0 \n", "66 4.0 8.0 ... 5.0 3.0 3.0 4.0 \n", "67 7.0 7.0 ... 5.0 5.0 9.0 6.0 \n", "68 5.0 7.0 ... 6.0 5.0 4.0 5.0 \n", "69 8.0 9.0 ... 9.0 9.0 8.0 6.0 \n", "70 6.0 8.0 ... 5.0 7.0 5.0 5.0 \n", "71 6.0 4.0 ... 4.0 2.0 6.0 8.0 \n", "72 8.0 8.0 ... 6.0 7.0 8.0 7.0 \n", "73 8.0 9.0 ... 5.0 4.0 8.0 8.0 \n", "74 3.0 6.0 ... 3.0 2.0 3.0 2.0 \n", "75 9.0 6.0 ... 5.0 5.0 5.0 5.0 \n", "76 6.0 8.0 ... 3.0 9.0 5.0 5.0 \n", "77 9.0 9.0 ... 6.0 8.0 9.0 5.0 \n", "78 5.0 4.0 ... 9.0 7.0 4.0 5.0 \n", "79 2.0 5.0 ... 4.0 5.0 5.0 5.0 \n", "\n", " chocolate_chips orange green_tea marron chocolate_mint adzuki-bean \n", "0 9.0 7.0 8.0 8.0 1.0 7.0 \n", "1 9.0 4.0 7.0 7.0 8.0 9.0 \n", "2 6.0 7.0 3.0 7.0 3.0 3.0 \n", "3 8.0 9.0 1.0 2.0 1.0 3.0 \n", "4 9.0 6.0 6.0 6.0 9.0 1.0 \n", "5 8.0 9.0 9.0 5.0 9.0 1.0 \n", "6 9.0 9.0 3.0 5.0 2.0 3.0 \n", "7 5.0 6.0 8.0 5.0 6.0 8.0 \n", "8 8.0 6.0 3.0 3.0 7.0 3.0 \n", "9 8.0 6.0 6.0 2.0 1.0 1.0 \n", "10 6.0 7.0 8.0 6.0 4.0 5.0 \n", "11 9.0 5.0 6.0 4.0 9.0 7.0 \n", "12 9.0 7.0 7.0 6.0 8.0 8.0 \n", "13 9.0 9.0 8.0 9.0 7.0 8.0 \n", "14 8.0 6.0 9.0 4.0 9.0 5.0 \n", "15 4.0 6.0 8.0 5.0 7.0 7.0 \n", "16 7.0 5.0 9.0 6.0 9.0 6.0 \n", "17 7.0 9.0 8.0 4.0 9.0 4.0 \n", "18 8.0 7.0 9.0 5.0 8.0 5.0 \n", "19 7.0 6.0 7.0 7.0 8.0 8.0 \n", "20 9.0 9.0 9.0 7.0 4.0 8.0 \n", "21 8.0 6.0 7.0 8.0 4.0 8.0 \n", "22 9.0 8.0 9.0 5.0 5.0 8.0 \n", "23 7.0 3.0 5.0 9.0 9.0 9.0 \n", "24 8.0 4.0 8.0 9.0 3.0 9.0 \n", "25 9.0 8.0 8.0 6.0 1.0 7.0 \n", "26 8.0 9.0 6.0 3.0 1.0 7.0 \n", "27 8.0 4.0 8.0 7.0 8.0 7.0 \n", "28 9.0 7.0 7.0 5.0 5.0 3.0 \n", "29 9.0 6.0 9.0 7.0 9.0 6.0 \n", ".. ... ... ... ... ... ... \n", "50 8.0 5.0 8.0 3.0 8.0 2.0 \n", "51 9.0 8.0 7.0 5.0 5.0 9.0 \n", "52 9.0 5.0 3.0 9.0 1.0 9.0 \n", "53 7.0 6.0 3.0 4.0 6.0 5.0 \n", "54 9.0 7.0 9.0 7.0 8.0 9.0 \n", "55 9.0 9.0 8.0 5.0 9.0 8.0 \n", "56 8.0 7.0 9.0 7.0 1.0 9.0 \n", "57 8.0 2.0 7.0 1.0 1.0 3.0 \n", "58 7.0 7.0 8.0 6.0 3.0 6.0 \n", "59 5.0 8.0 7.0 6.0 5.0 6.0 \n", "60 8.0 5.0 1.0 3.0 5.0 1.0 \n", "61 9.0 7.0 6.0 6.0 4.0 5.0 \n", "62 9.0 5.0 6.0 6.0 8.0 5.0 \n", "63 9.0 3.0 6.0 5.0 9.0 1.0 \n", "64 6.0 4.0 8.0 5.0 4.0 6.0 \n", "65 7.0 7.0 9.0 5.0 5.0 6.0 \n", "66 8.0 7.0 8.0 5.0 3.0 4.0 \n", "67 9.0 6.0 9.0 7.0 8.0 5.0 \n", "68 7.0 8.0 5.0 5.0 5.0 5.0 \n", "69 9.0 8.0 9.0 7.0 7.0 7.0 \n", "70 9.0 7.0 3.0 7.0 6.0 8.0 \n", "71 4.0 7.0 8.0 3.0 2.0 6.0 \n", "72 8.0 5.0 8.0 8.0 8.0 7.0 \n", "73 9.0 8.0 9.0 6.0 9.0 4.0 \n", "74 6.0 8.0 1.0 1.0 2.0 2.0 \n", "75 7.0 7.0 4.0 4.0 4.0 4.0 \n", "76 9.0 7.0 6.0 5.0 6.0 7.0 \n", "77 9.0 9.0 9.0 4.0 9.0 9.0 \n", "78 8.0 8.0 5.0 5.0 8.0 4.0 \n", "79 5.0 7.0 1.0 1.0 5.0 5.0 \n", "\n", "[80 rows x 26 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# できあがったMatrixTの中身の確認\n", "pd.DataFrame(matrixT, columns=val_names)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# アイスクリームの種類に関するデータは５行目以降なので、MatrixTを作り直してください。\n", "matrixT = []\n", "val_names = []\n", "ids = []\n", "gender = []\n", "sta = 5\n", "for i, line in enumerate(open(\"icecream_chosa.txt\")):\n", " if i == 0:\n", " for j, val in enumerate(line.split()):\n", " if j < sta:\n", " continue\n", " val_names.append(val)\n", " else:\n", " vec = []\n", " for j, val in enumerate(line.split()):\n", " if j == 0:\n", " ids.append(val)\n", " elif j == 1:\n", " gender.append(int(val))\n", " elif j < sta:\n", " continue\n", " else:\n", " vec.append(float(val))\n", " matrixT.append(vec)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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"[80 rows x 21 columns]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# できあがったMatrixTの中身の確認\n", "pd.DataFrame(matrixT, columns=val_names)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__課題9.4__：作成した MatrixT をもとに、アイスクリームの好みに関する相関係数を作成してください。また、その相関行列をカラーマップで図示してください。" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 相関行列を計算し表示する" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 得られた相関行列をカラーマップで表示する。" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "__課題9.5__：作成した matrixT をもとに、アイスクリームの好みに関する因子分析を実行し、８０人の男女を色分けして、第一因子と第二因子でプロットしてください。また、この結果から、アイスクリームの好みに関して言えることを考察してください。" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 因子分析の実行" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 第一因子と第二因子でプロットする" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__今日の課題提出__：計算結果を.ipynb ファイル（または.jsonファイル）として保存し、指定したアドレスまでメールしてください。メールタイトルは「__アイスクリーム統計学789__」とし、メール本文に学籍番号と氏名を明記のこと。また、感想も書いてくれると喜びます。今後の講義の改善につながるかも知れません。" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }