{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ロジスティック回帰" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**ロジスティック回帰**とは、\"確率\"について回帰分析を行いたいときに有効です。\n", "ロジスティック回帰を用いれば、必ず予想結果が0から1の範囲内に収まります。\n", "ですから、確率を予測・分析したいときによく用いられます。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## データセット\n", "---\n", "今回は、目的変数(Y)を品質、説明変数(X)をXXXとして解析を行う" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "データはUCI Machine Learning Repository（Wine Quality Data Set）から入手できます。\n", "\n", "このデータは、ポルトガルワインの一種である、西北端の大西洋に面するミーニョ(Minho)地方で作られたヴィーニョ・ヴェルデ（Vinho Verde）を測定したデータから構成されています。\n", "\n", "データセットは赤ワインと白ワインの２種類のデータ群から構成されており、ワイン（銘柄名は不明）ごとに測定された１１種類の成分データとそのワインの味を評価したグレード（数値）からなっています。なお、グレードは３人以上のワイン査定士が評価した結果の中間値です（グレードは０（とてもまずい）から１０（絶品）まで）（詳細はCortezら(2009)を参照してください）。\n", "データサイズはそれぞれ1599、4898です。変数一覧はつぎのとおりです。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 分析環境構築" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# 分析に必要なライブラリをインポート\n", "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## データセット読み込み" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

	fixed acidity;\"volatile acidity\";\"citric acid\";\"residual sugar\";\"chlorides\";\"free sulfur dioxide\";\"total sulfur dioxide\";\"density\";\"pH\";\"sulphates\";\"alcohol\";\"quality\"
0	7.4;0.7;0;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5
1	7.8;0.88;0;2.6;0.098;25;67;0.9968;3.2;0.68;9.8;5
2	7.8;0.76;0.04;2.3;0.092;15;54;0.997;3.26;0.65;...
3	11.2;0.28;0.56;1.9;0.075;17;60;0.998;3.16;0.58...
4	7.4;0.7;0;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5

\n", "

" ], "text/plain": [ " fixed acidity;\"volatile acidity\";\"citric acid\";\"residual sugar\";\"chlorides\";\"free sulfur dioxide\";\"total sulfur dioxide\";\"density\";\"pH\";\"sulphates\";\"alcohol\";\"quality\"\n", "0 7.4;0.7;0;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5 \n", "1 7.8;0.88;0;2.6;0.098;25;67;0.9968;3.2;0.68;9.8;5 \n", "2 7.8;0.76;0.04;2.3;0.092;15;54;0.997;3.26;0.65;... \n", "3 11.2;0.28;0.56;1.9;0.075;17;60;0.998;3.16;0.58... \n", "4 7.4;0.7;0;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 読み込んだデータセットをdfという名前のデータフレームに代入する\n", "df = pd.read_csv(\"../dataset/winequality-red.csv\")\n", "# データの先頭行を表示する\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 列ごとに表示されていないので、読み込み方を工夫する必要がある\n", "- ;（セミコロン）で列に分割することができそうだ\n", "- sep=';'を追記して、列に分割してみよう" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
0	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5
1	7.8	0.88	0.00	2.6	0.098	25.0	67.0	0.9968	3.20	0.68	9.8	5
2	7.8	0.76	0.04	2.3	0.092	15.0	54.0	0.9970	3.26	0.65	9.8	5
3	11.2	0.28	0.56	1.9	0.075	17.0	60.0	0.9980	3.16	0.58	9.8	6
4	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5

\n", "

" ], "text/plain": [ " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n", "0 7.4 0.70 0.00 1.9 0.076 \n", "1 7.8 0.88 0.00 2.6 0.098 \n", "2 7.8 0.76 0.04 2.3 0.092 \n", "3 11.2 0.28 0.56 1.9 0.075 \n", "4 7.4 0.70 0.00 1.9 0.076 \n", "\n", " free sulfur dioxide total sulfur dioxide density pH sulphates \\\n", "0 11.0 34.0 0.9978 3.51 0.56 \n", "1 25.0 67.0 0.9968 3.20 0.68 \n", "2 15.0 54.0 0.9970 3.26 0.65 \n", "3 17.0 60.0 0.9980 3.16 0.58 \n", "4 11.0 34.0 0.9978 3.51 0.56 \n", "\n", " alcohol quality \n", "0 9.4 5 \n", "1 9.8 5 \n", "2 9.8 5 \n", "3 9.8 6 \n", "4 9.4 5 " ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# データセットを読み込む\n", "df = pd.read_csv(\"../dataset/winequality-red.csv\", sep=\";\")\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- データテーブルとして綺麗に列を分割して表示されている- データテーブルとして綺麗に列を分割して表示されている" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 列名を調べる" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 下記のリンクをクリックすると、公開用のスプレッドシートに飛びます\n", "- [スプレッドシートで翻訳関数を使ってみよう](https://docs.google.com/spreadsheets/d/1q2uugJ5_jtUcJeI8nHAX90Aa3TastjLwYW9NySmQ6ag/edit?usp=sharing)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "| 英語 | google翻訳 | 修正した日本語訳 |\n", "|:-----------|------------:|:------------:|\n", "| fixed acidity | 固定酸度 | This |\n", "| volatile acidity | 揮発性の酸味 | This |\n", "| citric acid | クエン酸 | This |\n", "| residual sugar | 残留糖 | This |\n", "| chlorides | 塩化物 | This |\n", "| free sulfur dioxide | 遊離二酸化硫黄 | This |\n", "| total sulfur dioxide | 総二酸化硫黄 | This |\n", "| density | 密度 | This |\n", "| pH | pH | This |\n", "| sulphates | 硫酸塩 | This |\n", "| alcohol | アルコール | This |\n", "| quality | 品質 | This |" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 集計" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### データ量を把握する（shape）" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1599, 12)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- およそ1600件のデータである\n", "- 1599行, 12列のデータテーブル" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### データ個数を調べる（count）" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "fixed acidity 1599\n", "volatile acidity 1599\n", "citric acid 1599\n", "residual sugar 1599\n", "chlorides 1599\n", "free sulfur dioxide 1599\n", "total sulfur dioxide 1599\n", "density 1599\n", "pH 1599\n", "sulphates 1599\n", "alcohol 1599\n", "quality 1599\n", "dtype: int64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 今回は欠損はなさそう\n", "- 異常値が入っていないかは注意して進める" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 記述統計量（describe）" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
count	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000
mean	8.319637	0.527821	0.270976	2.538806	0.087467	15.874922	46.467792	0.996747	3.311113	0.658149	10.422983	5.636023
std	1.741096	0.179060	0.194801	1.409928	0.047065	10.460157	32.895324	0.001887	0.154386	0.169507	1.065668	0.807569
min	4.600000	0.120000	0.000000	0.900000	0.012000	1.000000	6.000000	0.990070	2.740000	0.330000	8.400000	3.000000
25%	7.100000	0.390000	0.090000	1.900000	0.070000	7.000000	22.000000	0.995600	3.210000	0.550000	9.500000	5.000000
50%	7.900000	0.520000	0.260000	2.200000	0.079000	14.000000	38.000000	0.996750	3.310000	0.620000	10.200000	6.000000
75%	9.200000	0.640000	0.420000	2.600000	0.090000	21.000000	62.000000	0.997835	3.400000	0.730000	11.100000	6.000000
max	15.900000	1.580000	1.000000	15.500000	0.611000	72.000000	289.000000	1.003690	4.010000	2.000000	14.900000	8.000000

\n", "

" ], "text/plain": [ " fixed acidity volatile acidity citric acid residual sugar \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 8.319637 0.527821 0.270976 2.538806 \n", "std 1.741096 0.179060 0.194801 1.409928 \n", "min 4.600000 0.120000 0.000000 0.900000 \n", "25% 7.100000 0.390000 0.090000 1.900000 \n", "50% 7.900000 0.520000 0.260000 2.200000 \n", "75% 9.200000 0.640000 0.420000 2.600000 \n", "max 15.900000 1.580000 1.000000 15.500000 \n", "\n", " chlorides free sulfur dioxide total sulfur dioxide density \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 0.087467 15.874922 46.467792 0.996747 \n", "std 0.047065 10.460157 32.895324 0.001887 \n", "min 0.012000 1.000000 6.000000 0.990070 \n", "25% 0.070000 7.000000 22.000000 0.995600 \n", "50% 0.079000 14.000000 38.000000 0.996750 \n", "75% 0.090000 21.000000 62.000000 0.997835 \n", "max 0.611000 72.000000 289.000000 1.003690 \n", "\n", " pH sulphates alcohol quality \n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 3.311113 0.658149 10.422983 5.636023 \n", "std 0.154386 0.169507 1.065668 0.807569 \n", "min 2.740000 0.330000 8.400000 3.000000 \n", "25% 3.210000 0.550000 9.500000 5.000000 \n", "50% 3.310000 0.620000 10.200000 6.000000 \n", "75% 3.400000 0.730000 11.100000 6.000000 \n", "max 4.010000 2.000000 14.900000 8.000000 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 今回は、ワインの美味しさを「品質スコア」と定義する\n", "- 「品質スコア」はデータテーブルでは、「quality」に格納されている\n", "- 「quality」の最大値と最小値はそれぞれ8と3である\n", "- 一般的に最小値3, 最大値8の選択肢はなさそうだ\n", "- なので、0~10段階の選択肢があると推測する\n", "- 「quality」の平均値は約5.64である\n", "- 0〜10の10段階で、平均が5.6であるなら、「quality」の値は正しそうだ\n", "- 「quality」の標準偏差は約0.81である\n", "- 後述" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## データ型" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "fixed acidity float64\n", "volatile acidity float64\n", "citric acid float64\n", "residual sugar float64\n", "chlorides float64\n", "free sulfur dioxide float64\n", "total sulfur dioxide float64\n", "density float64\n", "pH float64\n", "sulphates float64\n", "alcohol float64\n", "quality int64\n", "dtype: object" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 64bitの浮動小数点数なので数値\n", "- 数値データで構成されているため、モデルは作りやすいと予想できる" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 可視化" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 10., 0., 53., 0., 681., 0., 638., 0., 199., 18.]),\n", " array([3. , 3.5, 4. , 4.5, 5. , 5.5, 6. , 6.5, 7. , 7.5, 8. ]),\n", " )" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", " \n", "# 品質のヒストグラムを描画\n", "plt.hist(df.quality)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid character in identifier (, line 1)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m plt.scatter（df.quality）\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid character in identifier\n" ] } ], "source": [ "plt.scatter（df.quality）" ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
0	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5
1	7.8	0.88	0.00	2.6	0.098	25.0	67.0	0.9968	3.20	0.68	9.8	5
2	7.8	0.76	0.04	2.3	0.092	15.0	54.0	0.9970	3.26	0.65	9.8	5
3	11.2	0.28	0.56	1.9	0.075	17.0	60.0	0.9980	3.16	0.58	9.8	6
4	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5

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	0	1	2	3	4	5	6	7	8	9	10	11	12
count	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000
mean	0.329171	0.279329	0.270976	0.112247	0.125988	0.209506	0.142996	0.490211	0.449695	0.196496	0.311228	0.527205	0.135710
std	0.154079	0.122644	0.194801	0.096570	0.078573	0.147326	0.116238	0.138571	0.121564	0.101501	0.163949	0.161514	0.342587
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.221239	0.184932	0.090000	0.068493	0.096828	0.084507	0.056537	0.406021	0.370079	0.131737	0.169231	0.400000	0.000000
50%	0.292035	0.273973	0.260000	0.089041	0.111853	0.183099	0.113074	0.490455	0.448819	0.173653	0.276923	0.600000	0.000000
75%	0.407080	0.356164	0.420000	0.116438	0.130217	0.281690	0.197880	0.570117	0.519685	0.239521	0.415385	0.600000	0.000000
max	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000

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" ], "text/plain": [ " 0 1 2 3 4 \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 0.329171 0.279329 0.270976 0.112247 0.125988 \n", "std 0.154079 0.122644 0.194801 0.096570 0.078573 \n", "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.221239 0.184932 0.090000 0.068493 0.096828 \n", "50% 0.292035 0.273973 0.260000 0.089041 0.111853 \n", "75% 0.407080 0.356164 0.420000 0.116438 0.130217 \n", "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", "\n", " 5 6 7 8 9 \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 0.209506 0.142996 0.490211 0.449695 0.196496 \n", "std 0.147326 0.116238 0.138571 0.121564 0.101501 \n", "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.084507 0.056537 0.406021 0.370079 0.131737 \n", "50% 0.183099 0.113074 0.490455 0.448819 0.173653 \n", "75% 0.281690 0.197880 0.570117 0.519685 0.239521 \n", "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", "\n", " 10 11 12 \n", "count 1599.000000 1599.000000 1599.000000 \n", "mean 0.311228 0.527205 0.135710 \n", "std 0.163949 0.161514 0.342587 \n", "min 0.000000 0.000000 0.000000 \n", "25% 0.169231 0.400000 0.000000 \n", "50% 0.276923 0.600000 0.000000 \n", "75% 0.415385 0.600000 0.000000 \n", "max 1.000000 1.000000 1.000000 " ] }, "execution_count": 153, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# データ前処理に必要なライブラリ\n", "from sklearn import preprocessing\n", "\n", "# インスタンスの作成\n", "mmscaler = preprocessing.MinMaxScaler() \n", "\n", "# 説明変数の最大・最小を計算\n", "mmscaler.fit(df)\n", "df_mms = pd.DataFrame(mmscaler.transform(df))\n", "df_mms.describe()" ] }, { "cell_type": "code", "execution_count": 154, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
0	0.247788	0.397260	0.00	0.068493	0.106845	0.140845	0.098940	0.567548	0.606299	0.137725	0.153846	0.4
1	0.283186	0.520548	0.00	0.116438	0.143573	0.338028	0.215548	0.494126	0.362205	0.209581	0.215385	0.4
2	0.283186	0.438356	0.04	0.095890	0.133556	0.197183	0.169611	0.508811	0.409449	0.191617	0.215385	0.4
3	0.584071	0.109589	0.56	0.068493	0.105175	0.225352	0.190813	0.582232	0.330709	0.149701	0.215385	0.6
4	0.247788	0.397260	0.00	0.068493	0.106845	0.140845	0.098940	0.567548	0.606299	0.137725	0.153846	0.4

\n", "

" ], "text/plain": [ " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n", "0 0.247788 0.397260 0.00 0.068493 0.106845 \n", "1 0.283186 0.520548 0.00 0.116438 0.143573 \n", "2 0.283186 0.438356 0.04 0.095890 0.133556 \n", "3 0.584071 0.109589 0.56 0.068493 0.105175 \n", "4 0.247788 0.397260 0.00 0.068493 0.106845 \n", "\n", " free sulfur dioxide total sulfur dioxide density pH sulphates \\\n", "0 0.140845 0.098940 0.567548 0.606299 0.137725 \n", "1 0.338028 0.215548 0.494126 0.362205 0.209581 \n", "2 0.197183 0.169611 0.508811 0.409449 0.191617 \n", "3 0.225352 0.190813 0.582232 0.330709 0.149701 \n", "4 0.140845 0.098940 0.567548 0.606299 0.137725 \n", "\n", " alcohol quality delicious \n", "0 0.153846 0.4 0.0 \n", "1 0.215385 0.4 0.0 \n", "2 0.215385 0.4 0.0 \n", "3 0.215385 0.6 0.0 \n", "4 0.153846 0.4 0.0 " ] }, "execution_count": 154, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# スケール変換によって変わってしまった列名を直す\n", "df = df_mms.rename(columns={\n", " 0: 'fixed acidity', \n", " 1: 'volatile acidity', \n", " 2: 'citric acid', \n", " 3: 'residual sugar', \n", " 4: 'chlorides', \n", " 5: 'free sulfur dioxide', \n", " 6: 'total sulfur dioxide', \n", " 7: 'density', \n", " 8: 'pH', \n", " 9: 'sulphates', \n", " 10: 'alcohol', \n", " 11: 'quality', \n", " 12: 'delicious' \n", "})\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
delicious
0.0	0.321843	0.292481	0.254407	0.110419	0.129016	0.213693	0.149420	0.498470	0.452454	0.188475	0.284775	0.481766
1.0	0.375841	0.195568	0.376498	0.123887	0.106699	0.182839	0.102083	0.437612	0.432127	0.247579	0.479700	0.816590

\n", "

" ], "text/plain": [ " fixed acidity volatile acidity citric acid residual sugar \\\n", "delicious \n", "0.0 0.321843 0.292481 0.254407 0.110419 \n", "1.0 0.375841 0.195568 0.376498 0.123887 \n", "\n", " chlorides free sulfur dioxide total sulfur dioxide density \\\n", "delicious \n", "0.0 0.129016 0.213693 0.149420 0.498470 \n", "1.0 0.106699 0.182839 0.102083 0.437612 \n", "\n", " pH sulphates alcohol quality \n", "delicious \n", "0.0 0.452454 0.188475 0.284775 0.481766 \n", "1.0 0.432127 0.247579 0.479700 0.816590 " ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('delicious').mean()" ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality	delicious
count	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000
mean	0.329171	0.279329	0.270976	0.112247	0.125988	0.209506	0.142996	0.490211	0.449695	0.196496	0.311228	0.527205	0.135710
std	0.154079	0.122644	0.194801	0.096570	0.078573	0.147326	0.116238	0.138571	0.121564	0.101501	0.163949	0.161514	0.342587
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.221239	0.184932	0.090000	0.068493	0.096828	0.084507	0.056537	0.406021	0.370079	0.131737	0.169231	0.400000	0.000000
50%	0.292035	0.273973	0.260000	0.089041	0.111853	0.183099	0.113074	0.490455	0.448819	0.173653	0.276923	0.600000	0.000000
75%	0.407080	0.356164	0.420000	0.116438	0.130217	0.281690	0.197880	0.570117	0.519685	0.239521	0.415385	0.600000	0.000000
max	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000

\n", "

" ], "text/plain": [ " fixed acidity volatile acidity citric acid residual sugar \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 0.329171 0.279329 0.270976 0.112247 \n", "std 0.154079 0.122644 0.194801 0.096570 \n", "min 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.221239 0.184932 0.090000 0.068493 \n", "50% 0.292035 0.273973 0.260000 0.089041 \n", "75% 0.407080 0.356164 0.420000 0.116438 \n", "max 1.000000 1.000000 1.000000 1.000000 \n", "\n", " chlorides free sulfur dioxide total sulfur dioxide density \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 0.125988 0.209506 0.142996 0.490211 \n", "std 0.078573 0.147326 0.116238 0.138571 \n", "min 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.096828 0.084507 0.056537 0.406021 \n", "50% 0.111853 0.183099 0.113074 0.490455 \n", "75% 0.130217 0.281690 0.197880 0.570117 \n", "max 1.000000 1.000000 1.000000 1.000000 \n", "\n", " pH sulphates alcohol quality delicious \n", "count 1599.000000 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 0.449695 0.196496 0.311228 0.527205 0.135710 \n", "std 0.121564 0.101501 0.163949 0.161514 0.342587 \n", "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.370079 0.131737 0.169231 0.400000 0.000000 \n", "50% 0.448819 0.173653 0.276923 0.600000 0.000000 \n", "75% 0.519685 0.239521 0.415385 0.600000 0.000000 \n", "max 1.000000 1.000000 1.000000 1.000000 1.000000 " ] }, "execution_count": 131, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 学習データセット作成" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### データ分割" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [], "source": [ "# 教師データの作成\n", "Y = df2[\"delicious\"]\n", "X = df2.drop([\"quality\", \"delicious\"], axis=1)" ] }, { "cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False)" ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ロジスティク回帰\n", "from sklearn.linear_model import LogisticRegression\n", "# 交差検証\n", "from sklearn.cross_validation import train_test_split\n", "# インスタンス作成\n", "log_model = LogisticRegression()\n", "\n", "# データを分割します。テストが全体の40%になるようにします。\n", "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.7,random_state=3)\n", "\n", "# データを使って学習します。\n", "log_model.fit(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol
306	0.265487	0.342466	0.32	0.089041	0.116861	0.084507	0.169611	0.479442	0.488189	0.113772	0.153846
940	0.442478	0.143836	0.52	0.089041	0.103506	0.169014	0.067138	0.368576	0.488189	0.257485	0.615385
129	0.132743	0.178082	0.15	0.061644	0.100167	0.070423	0.045936	0.398678	0.535433	0.143713	0.153846
395	0.654867	0.171233	0.76	0.226027	0.090150	0.084507	0.113074	0.758443	0.377953	0.161677	0.707692
1462	0.194690	0.356164	0.03	0.095890	0.105175	0.183099	0.088339	0.395007	0.488189	0.149701	0.307692
326	0.619469	0.280822	0.66	0.188356	0.181970	0.070423	0.028269	0.567548	0.244094	0.245509	0.476923
191	0.159292	0.171233	0.25	0.068493	0.103506	0.281690	0.151943	0.538179	0.653543	0.173653	0.215385
722	0.265487	0.205479	0.08	0.123288	0.120200	0.197183	0.148410	0.494126	0.370079	0.155689	0.246154
1564	0.230088	0.393836	0.13	0.075342	0.106845	0.154930	0.049470	0.395742	0.433071	0.125749	0.261538
1064	0.318584	0.424658	0.09	0.075342	0.091820	0.056338	0.014134	0.301762	0.425197	0.143713	0.523077
993	0.221239	0.150685	0.28	0.075342	0.116861	0.422535	0.219081	0.504405	0.559055	0.089820	0.153846
920	0.442478	0.198630	0.37	0.095890	0.131886	0.126761	0.060071	0.571953	0.393701	0.137725	0.323077
495	0.539823	0.157534	0.53	0.116438	0.096828	0.056338	0.035336	0.523495	0.322835	0.191617	0.400000
1032	0.309735	0.479452	0.00	0.219178	0.138564	0.056338	0.028269	0.621880	0.488189	0.119760	0.184615
867	0.203540	0.267123	0.23	0.075342	0.100167	0.169014	0.056537	0.316446	0.519685	0.305389	0.430769
384	0.274336	0.267123	0.28	0.082192	0.125209	0.309859	0.169611	0.582232	0.535433	0.245509	0.123077
1167	0.318584	0.143836	0.39	0.109589	0.103506	0.394366	0.148410	0.382526	0.456693	0.329341	0.615385
652	1.000000	0.164384	0.65	0.452055	0.140234	0.295775	0.229682	0.552863	0.188976	0.305389	1.000000
1312	0.300885	0.726027	0.21	0.068493	0.118531	0.183099	0.123675	0.385463	0.472441	0.083832	0.323077
397	0.610619	0.321918	0.59	0.116438	0.125209	0.169014	0.151943	0.640969	0.346457	0.191617	0.400000
946	0.495575	0.219178	0.58	0.219178	0.133556	0.140845	0.063604	0.541850	0.433071	0.395210	0.553846
1206	0.230088	0.164384	0.46	0.082192	0.103506	0.323944	0.134276	0.386931	0.519685	0.311377	0.400000
1566	0.185841	0.027397	0.64	0.082192	0.078464	0.323944	0.162544	0.357562	0.472441	0.227545	0.430769
724	0.256637	0.681507	0.10	0.150685	0.123539	0.056338	0.021201	0.420705	0.629921	0.161677	0.430769
532	0.646018	0.178082	0.49	0.123288	0.143573	0.154930	0.127208	0.758443	0.330709	0.167665	0.292308
762	0.371681	0.397260	0.00	0.054795	0.095159	0.098592	0.045936	0.509545	0.448819	0.119760	0.246154
161	0.265487	0.383562	0.02	0.027397	0.100167	0.112676	0.049470	0.472100	0.338583	0.449102	0.123077
1322	0.398230	0.150685	0.42	0.061644	0.076795	0.112676	0.042403	0.282673	0.346457	0.131737	0.461538
1103	0.247788	0.253425	0.27	0.082192	0.098497	0.183099	0.067138	0.279736	0.480315	0.179641	0.553846
936	0.371681	0.123288	0.38	0.095890	0.080134	0.253521	0.233216	0.393539	0.511811	0.233533	0.523077
...	...	...	...	...	...	...	...	...	...	...	...
1176	0.168142	0.520548	0.03	0.321918	0.111853	0.309859	0.144876	0.414831	0.661417	0.101796	0.430769
413	0.469027	0.191781	0.53	0.397260	0.141903	0.070423	0.045936	0.626285	0.417323	0.293413	0.507692
622	0.477876	0.315068	0.22	0.068493	0.113523	0.112676	0.091873	0.538179	0.307087	0.131737	0.169231
872	0.238938	0.157534	0.24	0.075342	0.091820	0.380282	0.148410	0.417768	0.543307	0.125749	0.246154
858	0.628319	0.109589	0.47	0.054795	0.070117	0.225352	0.091873	0.498532	0.322835	0.203593	0.338462
641	0.433628	0.321918	0.44	0.095890	0.098497	0.281690	0.219081	0.670338	0.566929	0.179641	0.169231
1340	0.256637	0.267123	0.02	0.054795	0.120200	0.169014	0.088339	0.389868	0.488189	0.125749	0.323077
258	0.274336	0.198630	0.76	0.061644	1.000000	0.098592	0.137809	0.494126	0.251969	0.556886	0.153846
834	0.371681	0.386986	0.26	0.047945	0.126878	0.211268	0.060071	0.504405	0.456693	0.083832	0.153846
662	0.230088	0.308219	0.06	0.047945	0.106845	0.112676	0.074205	0.523495	0.488189	0.221557	0.184615
1498	0.176991	0.530822	0.04	0.095890	0.093489	0.084507	0.024735	0.422173	0.622047	0.149701	0.369231
337	0.283186	0.212329	0.32	0.130137	0.113523	0.394366	0.183746	0.538179	0.448819	0.185629	0.292308
1562	0.230088	0.393836	0.13	0.075342	0.106845	0.154930	0.049470	0.395742	0.433071	0.125749	0.261538
1528	0.292035	0.116438	0.49	0.089041	0.140234	0.281690	0.187279	0.519090	0.448819	0.203593	0.261538
398	0.610619	0.321918	0.59	0.116438	0.125209	0.169014	0.151943	0.640969	0.346457	0.191617	0.400000
1447	0.194690	0.376712	0.00	0.068493	0.113523	0.295775	0.116608	0.509545	0.519685	0.245509	0.200000
605	0.327434	0.328767	0.13	0.116438	0.121870	0.070423	0.063604	0.611601	0.448819	0.155689	0.123077
480	0.530973	0.109589	0.39	1.000000	0.095159	0.070423	0.060071	0.919971	0.299213	0.197605	0.123077
1446	0.203540	0.349315	0.02	0.068493	0.110184	0.239437	0.084806	0.517621	0.519685	0.251497	0.215385
1143	0.212389	0.068493	0.30	0.061644	0.088481	0.211268	0.049470	0.488253	0.685039	0.293413	0.246154
917	0.194690	0.198630	0.31	0.541096	0.120200	0.352113	0.137809	0.599853	0.503937	0.185629	0.261538
1017	0.300885	0.041096	0.37	0.000000	0.061770	0.492958	0.363958	0.000000	0.118110	0.065868	0.661538
875	0.371681	0.130137	0.40	0.130137	0.161937	0.084507	0.035336	0.445668	0.448819	0.275449	0.523077
659	0.221239	0.493151	0.02	0.239726	0.140234	0.056338	0.024735	0.508811	0.527559	0.143713	0.400000
1021	0.592920	0.164384	0.66	0.102740	0.185309	0.028169	0.007067	0.466226	0.362205	0.119760	0.538462
789	0.353982	0.349315	0.17	0.136986	0.145242	0.281690	0.399293	0.582232	0.275591	0.113772	0.138462
256	0.424779	0.150685	0.37	0.089041	0.105175	0.056338	0.024735	0.582232	0.377953	0.173653	0.123077
968	0.389381	0.191781	0.43	0.102740	0.093489	0.394366	0.141343	0.310573	0.362205	0.161677	0.584615
952	0.318584	0.130137	0.40	0.089041	0.076795	0.070423	0.014134	0.388399	0.448819	0.209581	0.430769
1273	0.256637	0.315068	0.20	0.075342	0.101836	0.464789	0.134276	0.357562	0.283465	0.059880	0.138462

\n", "

479 rows × 11 columns

\n", "

" ], "text/plain": [ " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n", "306 0.265487 0.342466 0.32 0.089041 0.116861 \n", "940 0.442478 0.143836 0.52 0.089041 0.103506 \n", "129 0.132743 0.178082 0.15 0.061644 0.100167 \n", "395 0.654867 0.171233 0.76 0.226027 0.090150 \n", "1462 0.194690 0.356164 0.03 0.095890 0.105175 \n", "326 0.619469 0.280822 0.66 0.188356 0.181970 \n", "191 0.159292 0.171233 0.25 0.068493 0.103506 \n", "722 0.265487 0.205479 0.08 0.123288 0.120200 \n", "1564 0.230088 0.393836 0.13 0.075342 0.106845 \n", "1064 0.318584 0.424658 0.09 0.075342 0.091820 \n", "993 0.221239 0.150685 0.28 0.075342 0.116861 \n", "920 0.442478 0.198630 0.37 0.095890 0.131886 \n", "495 0.539823 0.157534 0.53 0.116438 0.096828 \n", "1032 0.309735 0.479452 0.00 0.219178 0.138564 \n", "867 0.203540 0.267123 0.23 0.075342 0.100167 \n", "384 0.274336 0.267123 0.28 0.082192 0.125209 \n", "1167 0.318584 0.143836 0.39 0.109589 0.103506 \n", "652 1.000000 0.164384 0.65 0.452055 0.140234 \n", "1312 0.300885 0.726027 0.21 0.068493 0.118531 \n", "397 0.610619 0.321918 0.59 0.116438 0.125209 \n", "946 0.495575 0.219178 0.58 0.219178 0.133556 \n", "1206 0.230088 0.164384 0.46 0.082192 0.103506 \n", "1566 0.185841 0.027397 0.64 0.082192 0.078464 \n", "724 0.256637 0.681507 0.10 0.150685 0.123539 \n", "532 0.646018 0.178082 0.49 0.123288 0.143573 \n", "762 0.371681 0.397260 0.00 0.054795 0.095159 \n", "161 0.265487 0.383562 0.02 0.027397 0.100167 \n", "1322 0.398230 0.150685 0.42 0.061644 0.076795 \n", "1103 0.247788 0.253425 0.27 0.082192 0.098497 \n", "936 0.371681 0.123288 0.38 0.095890 0.080134 \n", "... ... ... ... ... ... \n", "1176 0.168142 0.520548 0.03 0.321918 0.111853 \n", "413 0.469027 0.191781 0.53 0.397260 0.141903 \n", "622 0.477876 0.315068 0.22 0.068493 0.113523 \n", "872 0.238938 0.157534 0.24 0.075342 0.091820 \n", "858 0.628319 0.109589 0.47 0.054795 0.070117 \n", "641 0.433628 0.321918 0.44 0.095890 0.098497 \n", "1340 0.256637 0.267123 0.02 0.054795 0.120200 \n", "258 0.274336 0.198630 0.76 0.061644 1.000000 \n", "834 0.371681 0.386986 0.26 0.047945 0.126878 \n", "662 0.230088 0.308219 0.06 0.047945 0.106845 \n", "1498 0.176991 0.530822 0.04 0.095890 0.093489 \n", "337 0.283186 0.212329 0.32 0.130137 0.113523 \n", "1562 0.230088 0.393836 0.13 0.075342 0.106845 \n", "1528 0.292035 0.116438 0.49 0.089041 0.140234 \n", "398 0.610619 0.321918 0.59 0.116438 0.125209 \n", "1447 0.194690 0.376712 0.00 0.068493 0.113523 \n", "605 0.327434 0.328767 0.13 0.116438 0.121870 \n", "480 0.530973 0.109589 0.39 1.000000 0.095159 \n", "1446 0.203540 0.349315 0.02 0.068493 0.110184 \n", "1143 0.212389 0.068493 0.30 0.061644 0.088481 \n", "917 0.194690 0.198630 0.31 0.541096 0.120200 \n", "1017 0.300885 0.041096 0.37 0.000000 0.061770 \n", "875 0.371681 0.130137 0.40 0.130137 0.161937 \n", "659 0.221239 0.493151 0.02 0.239726 0.140234 \n", "1021 0.592920 0.164384 0.66 0.102740 0.185309 \n", "789 0.353982 0.349315 0.17 0.136986 0.145242 \n", "256 0.424779 0.150685 0.37 0.089041 0.105175 \n", "968 0.389381 0.191781 0.43 0.102740 0.093489 \n", "952 0.318584 0.130137 0.40 0.089041 0.076795 \n", "1273 0.256637 0.315068 0.20 0.075342 0.101836 \n", "\n", " free sulfur dioxide total sulfur dioxide density pH \\\n", "306 0.084507 0.169611 0.479442 0.488189 \n", "940 0.169014 0.067138 0.368576 0.488189 \n", "129 0.070423 0.045936 0.398678 0.535433 \n", "395 0.084507 0.113074 0.758443 0.377953 \n", "1462 0.183099 0.088339 0.395007 0.488189 \n", "326 0.070423 0.028269 0.567548 0.244094 \n", "191 0.281690 0.151943 0.538179 0.653543 \n", "722 0.197183 0.148410 0.494126 0.370079 \n", "1564 0.154930 0.049470 0.395742 0.433071 \n", "1064 0.056338 0.014134 0.301762 0.425197 \n", "993 0.422535 0.219081 0.504405 0.559055 \n", "920 0.126761 0.060071 0.571953 0.393701 \n", "495 0.056338 0.035336 0.523495 0.322835 \n", "1032 0.056338 0.028269 0.621880 0.488189 \n", "867 0.169014 0.056537 0.316446 0.519685 \n", "384 0.309859 0.169611 0.582232 0.535433 \n", "1167 0.394366 0.148410 0.382526 0.456693 \n", "652 0.295775 0.229682 0.552863 0.188976 \n", "1312 0.183099 0.123675 0.385463 0.472441 \n", "397 0.169014 0.151943 0.640969 0.346457 \n", "946 0.140845 0.063604 0.541850 0.433071 \n", "1206 0.323944 0.134276 0.386931 0.519685 \n", "1566 0.323944 0.162544 0.357562 0.472441 \n", "724 0.056338 0.021201 0.420705 0.629921 \n", "532 0.154930 0.127208 0.758443 0.330709 \n", "762 0.098592 0.045936 0.509545 0.448819 \n", "161 0.112676 0.049470 0.472100 0.338583 \n", "1322 0.112676 0.042403 0.282673 0.346457 \n", "1103 0.183099 0.067138 0.279736 0.480315 \n", "936 0.253521 0.233216 0.393539 0.511811 \n", "... ... ... ... ... \n", "1176 0.309859 0.144876 0.414831 0.661417 \n", "413 0.070423 0.045936 0.626285 0.417323 \n", "622 0.112676 0.091873 0.538179 0.307087 \n", "872 0.380282 0.148410 0.417768 0.543307 \n", "858 0.225352 0.091873 0.498532 0.322835 \n", "641 0.281690 0.219081 0.670338 0.566929 \n", "1340 0.169014 0.088339 0.389868 0.488189 \n", "258 0.098592 0.137809 0.494126 0.251969 \n", "834 0.211268 0.060071 0.504405 0.456693 \n", "662 0.112676 0.074205 0.523495 0.488189 \n", "1498 0.084507 0.024735 0.422173 0.622047 \n", "337 0.394366 0.183746 0.538179 0.448819 \n", "1562 0.154930 0.049470 0.395742 0.433071 \n", "1528 0.281690 0.187279 0.519090 0.448819 \n", "398 0.169014 0.151943 0.640969 0.346457 \n", "1447 0.295775 0.116608 0.509545 0.519685 \n", "605 0.070423 0.063604 0.611601 0.448819 \n", "480 0.070423 0.060071 0.919971 0.299213 \n", "1446 0.239437 0.084806 0.517621 0.519685 \n", "1143 0.211268 0.049470 0.488253 0.685039 \n", "917 0.352113 0.137809 0.599853 0.503937 \n", "1017 0.492958 0.363958 0.000000 0.118110 \n", "875 0.084507 0.035336 0.445668 0.448819 \n", "659 0.056338 0.024735 0.508811 0.527559 \n", "1021 0.028169 0.007067 0.466226 0.362205 \n", "789 0.281690 0.399293 0.582232 0.275591 \n", "256 0.056338 0.024735 0.582232 0.377953 \n", "968 0.394366 0.141343 0.310573 0.362205 \n", "952 0.070423 0.014134 0.388399 0.448819 \n", "1273 0.464789 0.134276 0.357562 0.283465 \n", "\n", " sulphates alcohol \n", "306 0.113772 0.153846 \n", "940 0.257485 0.615385 \n", "129 0.143713 0.153846 \n", "395 0.161677 0.707692 \n", "1462 0.149701 0.307692 \n", "326 0.245509 0.476923 \n", "191 0.173653 0.215385 \n", "722 0.155689 0.246154 \n", "1564 0.125749 0.261538 \n", "1064 0.143713 0.523077 \n", "993 0.089820 0.153846 \n", "920 0.137725 0.323077 \n", "495 0.191617 0.400000 \n", "1032 0.119760 0.184615 \n", "867 0.305389 0.430769 \n", "384 0.245509 0.123077 \n", "1167 0.329341 0.615385 \n", "652 0.305389 1.000000 \n", "1312 0.083832 0.323077 \n", "397 0.191617 0.400000 \n", "946 0.395210 0.553846 \n", "1206 0.311377 0.400000 \n", "1566 0.227545 0.430769 \n", "724 0.161677 0.430769 \n", "532 0.167665 0.292308 \n", "762 0.119760 0.246154 \n", "161 0.449102 0.123077 \n", "1322 0.131737 0.461538 \n", "1103 0.179641 0.553846 \n", "936 0.233533 0.523077 \n", "... ... ... \n", "1176 0.101796 0.430769 \n", "413 0.293413 0.507692 \n", "622 0.131737 0.169231 \n", "872 0.125749 0.246154 \n", "858 0.203593 0.338462 \n", "641 0.179641 0.169231 \n", "1340 0.125749 0.323077 \n", "258 0.556886 0.153846 \n", "834 0.083832 0.153846 \n", "662 0.221557 0.184615 \n", "1498 0.149701 0.369231 \n", "337 0.185629 0.292308 \n", "1562 0.125749 0.261538 \n", "1528 0.203593 0.261538 \n", "398 0.191617 0.400000 \n", "1447 0.245509 0.200000 \n", "605 0.155689 0.123077 \n", "480 0.197605 0.123077 \n", "1446 0.251497 0.215385 \n", "1143 0.293413 0.246154 \n", "917 0.185629 0.261538 \n", "1017 0.065868 0.661538 \n", "875 0.275449 0.523077 \n", "659 0.143713 0.400000 \n", "1021 0.119760 0.538462 \n", "789 0.113772 0.138462 \n", "256 0.173653 0.123077 \n", "968 0.161677 0.584615 \n", "952 0.209581 0.430769 \n", "1273 0.059880 0.138462 \n", "\n", "[479 rows x 11 columns]" ] }, "execution_count": 145, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train" ] }, { "cell_type": "raw", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.86875\n" ] } ], "source": [ "#テストデータを使って、モデルの精度を確認してみましょう。\n", "# 精度を計算するのに便利なツールです。\n", "from sklearn import metrics\n", "\n", "# テストデータを予測します。\n", "Y_pred = log_model.predict(X_test)\n", "\n", "# 精度を計算してみましょう。\n", "print(metrics.accuracy_score(Y_test,Y_pred))" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [], "source": [ "# pickleのインポート\n", "import pickle\n", "#pickleでモデルを保存する\n", "# モデルを保存する\n", "filename = 'nullsuckAi_01model.sav'\n", "pickle.dump(log_model, open(filename, 'wb'))" ] }, { "cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "coefficient = [[ 0.12036049 -1.69517452 1.16998293 0.31372357 -0.66999353 -0.6095375\n", " -1.2102646 -1.53659541 -1.09894278 1.60767685 3.37327056]]\n", "intercept = [-1.84404977]\n" ] } ], "source": [ "print(\"coefficient = \", log_model.coef_)\n", "print(\"intercept = \", log_model.intercept_)" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0.9405236 0.0594764 ]\n", " [0.86155829 0.13844171]\n", " [0.87360635 0.12639365]\n", " ...\n", " [0.78483507 0.21516493]\n", " [0.73678618 0.26321382]\n", " [0.96465211 0.03534789]]\n" ] } ], "source": [ "probs = log_model.predict_proba(X_test)\n", "print(probs)" ] }, { "cell_type": "code", "execution_count": 138, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from sklearn.calibration import calibration_curve\n", "\n", "prob = log_model.predict_proba(X_test)[:, 1] # 目的変数が1である確率を予測\n", "prob_true, prob_pred = calibration_curve(y_true=Y_test, y_prob=prob, n_bins=20)\n", "\n", "fig, ax1 = plt.subplots()\n", "ax1.plot(prob_pred, prob_true, marker='s', label='calibration plot', color='skyblue') # キャリプレーションプロットを作成\n", "ax1.plot([0, 1], [0, 1], linestyle='--', label='ideal', color='limegreen') # 45度線をプロット\n", "ax1.legend(bbox_to_anchor=(1.12, 1), loc='upper left')\n", "ax2 = ax1.twinx() # 2軸を追加\n", "ax2.hist(prob, bins=20, histtype='step', color='orangered') # スコアのヒストグラムも併せてプロット\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 149, "metadata": {}, "outputs": [], "source": [ "sample_wine1 = pd.DataFrame({\n", " 'fixed acidity': [0.3],\n", " 'volatile acidity': [0],\n", " 'citric acid': [0],\n", " 'residual sugar': [1],\n", " 'chlorides': [0],\n", " 'free sulfur dioxide': [0],\n", " 'total sulfur dioxide': [0],\n", " 'density': [0],\n", " 'pH': [0],\n", " 'sulphates': [0],\n", " 'alcohol': [1] \n", "})" ] }, { "cell_type": "code", "execution_count": 151, "metadata": {}, "outputs": [], "source": [ "sample_wine2 = pd.DataFrame({\n", " 'fixed acidity': [0],\n", " 'volatile acidity': [0],\n", " 'citric acid': [0],\n", " 'residual sugar': [0],\n", " 'chlorides': [0],\n", " 'free sulfur dioxide': [0],\n", " 'total sulfur dioxide': [0],\n", " 'density': [0],\n", " 'pH': [0],\n", " 'sulphates': [0],\n", " 'alcohol': [0] \n", "})" ] }, { "cell_type": "code", "execution_count": 150, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "美味しいワインに分類される可能性は 86.75 % です。\n" ] } ], "source": [ "spot_predict = log_model.predict_proba(sample_wine1)[0][1] #log_modelが分類される確率を予測\n", "print('美味しいワインに分類される可能性は {:.2f} % です。'.format(spot_predict * 100))" ] }, { "cell_type": "code", "execution_count": 152, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "美味しいワインに分類される可能性は 13.66 % です。\n" ] } ], "source": [ "spot_predict = log_model.predict_proba(sample_wine2)[0][1] #log_modelが分類される確率を予測\n", "print('美味しいワインに分類される可能性は {:.2f} % です。'.format(spot_predict * 100))" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol
quality
5	7.4	0.700	0.00	1.9	0.076	11.0	34.0	0.99780	3.51	0.56	9.4
5	7.8	0.880	0.00	2.6	0.098	25.0	67.0	0.99680	3.20	0.68	9.8
5	7.8	0.760	0.04	2.3	0.092	15.0	54.0	0.99700	3.26	0.65	9.8
6	11.2	0.280	0.56	1.9	0.075	17.0	60.0	0.99800	3.16	0.58	9.8
5	7.4	0.700	0.00	1.9	0.076	11.0	34.0	0.99780	3.51	0.56	9.4
5	7.4	0.660	0.00	1.8	0.075	13.0	40.0	0.99780	3.51	0.56	9.4
5	7.9	0.600	0.06	1.6	0.069	15.0	59.0	0.99640	3.30	0.46	9.4
7	7.3	0.650	0.00	1.2	0.065	15.0	21.0	0.99460	3.39	0.47	10.0
7	7.8	0.580	0.02	2.0	0.073	9.0	18.0	0.99680	3.36	0.57	9.5
5	7.5	0.500	0.36	6.1	0.071	17.0	102.0	0.99780	3.35	0.80	10.5
5	6.7	0.580	0.08	1.8	0.097	15.0	65.0	0.99590	3.28	0.54	9.2
5	7.5	0.500	0.36	6.1	0.071	17.0	102.0	0.99780	3.35	0.80	10.5
5	5.6	0.615	0.00	1.6	0.089	16.0	59.0	0.99430	3.58	0.52	9.9
5	7.8	0.610	0.29	1.6	0.114	9.0	29.0	0.99740	3.26	1.56	9.1
5	8.9	0.620	0.18	3.8	0.176	52.0	145.0	0.99860	3.16	0.88	9.2
5	8.9	0.620	0.19	3.9	0.170	51.0	148.0	0.99860	3.17	0.93	9.2
7	8.5	0.280	0.56	1.8	0.092	35.0	103.0	0.99690	3.30	0.75	10.5
5	8.1	0.560	0.28	1.7	0.368	16.0	56.0	0.99680	3.11	1.28	9.3
4	7.4	0.590	0.08	4.4	0.086	6.0	29.0	0.99740	3.38	0.50	9.0
6	7.9	0.320	0.51	1.8	0.341	17.0	56.0	0.99690	3.04	1.08	9.2
6	8.9	0.220	0.48	1.8	0.077	29.0	60.0	0.99680	3.39	0.53	9.4
5	7.6	0.390	0.31	2.3	0.082	23.0	71.0	0.99820	3.52	0.65	9.7
5	7.9	0.430	0.21	1.6	0.106	10.0	37.0	0.99660	3.17	0.91	9.5
5	8.5	0.490	0.11	2.3	0.084	9.0	67.0	0.99680	3.17	0.53	9.4
6	6.9	0.400	0.14	2.4	0.085	21.0	40.0	0.99680	3.43	0.63	9.7
5	6.3	0.390	0.16	1.4	0.080	11.0	23.0	0.99550	3.34	0.56	9.3
5	7.6	0.410	0.24	1.8	0.080	4.0	11.0	0.99620	3.28	0.59	9.5
5	7.9	0.430	0.21	1.6	0.106	10.0	37.0	0.99660	3.17	0.91	9.5
5	7.1	0.710	0.00	1.9	0.080	14.0	35.0	0.99720	3.47	0.55	9.4
6	7.8	0.645	0.00	2.0	0.082	8.0	16.0	0.99640	3.38	0.59	9.8
...	...	...	...	...	...	...	...	...	...	...	...
6	6.2	0.510	0.14	1.9	0.056	15.0	34.0	0.99396	3.48	0.57	11.5
6	6.4	0.360	0.53	2.2	0.230	19.0	35.0	0.99340	3.37	0.93	12.4
6	6.4	0.380	0.14	2.2	0.038	15.0	25.0	0.99514	3.44	0.65	11.1
5	7.3	0.690	0.32	2.2	0.069	35.0	104.0	0.99632	3.33	0.51	9.5
6	6.0	0.580	0.20	2.4	0.075	15.0	50.0	0.99467	3.58	0.67	12.5
6	5.6	0.310	0.78	13.9	0.074	23.0	92.0	0.99677	3.39	0.48	10.5
6	7.5	0.520	0.40	2.2	0.060	12.0	20.0	0.99474	3.26	0.64	11.8
6	8.0	0.300	0.63	1.6	0.081	16.0	29.0	0.99588	3.30	0.78	10.8
6	6.2	0.700	0.15	5.1	0.076	13.0	27.0	0.99622	3.54	0.60	11.9
6	6.8	0.670	0.15	1.8	0.118	13.0	20.0	0.99540	3.42	0.67	11.3
5	6.2	0.560	0.09	1.7	0.053	24.0	32.0	0.99402	3.54	0.60	11.3
6	7.4	0.350	0.33	2.4	0.068	9.0	26.0	0.99470	3.36	0.60	11.9
5	6.2	0.560	0.09	1.7	0.053	24.0	32.0	0.99402	3.54	0.60	11.3
5	6.1	0.715	0.10	2.6	0.053	13.0	27.0	0.99362	3.57	0.50	11.9
5	6.2	0.460	0.29	2.1	0.074	32.0	98.0	0.99578	3.33	0.62	9.8
7	6.7	0.320	0.44	2.4	0.061	24.0	34.0	0.99484	3.29	0.80	11.6
6	7.2	0.390	0.44	2.6	0.066	22.0	48.0	0.99494	3.30	0.84	11.5
6	7.5	0.310	0.41	2.4	0.065	34.0	60.0	0.99492	3.34	0.85	11.4
6	5.8	0.610	0.11	1.8	0.066	18.0	28.0	0.99483	3.55	0.66	10.9
6	7.2	0.660	0.33	2.5	0.068	34.0	102.0	0.99414	3.27	0.78	12.8
5	6.6	0.725	0.20	7.8	0.073	29.0	79.0	0.99770	3.29	0.54	9.2
6	6.3	0.550	0.15	1.8	0.077	26.0	35.0	0.99314	3.32	0.82	11.6
6	5.4	0.740	0.09	1.7	0.089	16.0	26.0	0.99402	3.67	0.56	11.6
6	6.3	0.510	0.13	2.3	0.076	29.0	40.0	0.99574	3.42	0.75	11.0
6	6.8	0.620	0.08	1.9	0.068	28.0	38.0	0.99651	3.42	0.82	9.5
5	6.2	0.600	0.08	2.0	0.090	32.0	44.0	0.99490	3.45	0.58	10.5
6	5.9	0.550	0.10	2.2	0.062	39.0	51.0	0.99512	3.52	0.76	11.2
6	6.3	0.510	0.13	2.3	0.076	29.0	40.0	0.99574	3.42	0.75	11.0
5	5.9	0.645	0.12	2.0	0.075	32.0	44.0	0.99547	3.57	0.71	10.2
6	6.0	0.310	0.47	3.6	0.067	18.0	42.0	0.99549	3.39	0.66	11.0

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1599 rows × 11 columns

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