{"id":1229,"date":"2019-07-27T20:15:52","date_gmt":"2019-07-27T11:15:52","guid":{"rendered":"https:\/\/tnishimaki.com\/?p=1229"},"modified":"2025-06-12T19:24:32","modified_gmt":"2025-06-12T10:24:32","slug":"python%e3%81%ab%e3%82%88%e3%82%8b%e3%83%ad%e3%82%b8%e3%82%b9%e3%83%86%e3%82%a3%e3%83%83%e3%82%af%e5%9b%9e%e5%b8%b0%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"https:\/\/analysis-navi.com\/?p=1229","title":{"rendered":"Python\u306b\u3088\u308b\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u5206\u6790"},"content":{"rendered":"\n

\u4f8b\u3048\u3070\u3001\u30b9\u30de\u30db\u30a2\u30d7\u30ea\u3092\u4f5c\u3063\u3066\u30ea\u30ea\u30fc\u30b9\u306f\u3057\u305f\u3082\u306e\u306e\u3001\u4e00\u4f53\u3001\u3069\u3093\u306a\u30e6\u30fc\u30b6\u304c\u767b\u9332\u3057\u3066\u304f\u308c\u308b\u306e\u304b\u3002
\u6027\u5225\u3001\u5e74\u9f62\u30fb\u30fb\u30fb\u306a\u3069\u3001\u4e00\u4f53\u3069\u3093\u306a\u50be\u5411\u3092\u6301\u3063\u305f\u4eba\u304c\u30e6\u30fc\u30b6\u767b\u9332\u3057\u3066\u304f\u308c\u308b\u50be\u5411\u306b\u3042\u308b\u306e\u304b\u3002
\u305d\u308c\u304c\u5206\u304b\u308c\u3070\u69d8\u3005\u306a\u30de\u30fc\u30b1\u30c6\u30a3\u30f3\u30b0\u306e\u624b\u304c\u6253\u3066\u307e\u3059\u3002<\/p>\n\n\n\n

\u305d\u3093\u306a\u8ab2\u984c\u3092\u89e3\u6c7a\u3057\u3066\u304f\u308c\u308b\u30c7\u30fc\u30bf\u5206\u6790\u624b\u6cd5\u304c\u300c\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u5206\u6790\u300d<\/span><\/strong>\u3067\u3059\u3002
\u672c\u8a18\u4e8b\u3067\u306f\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u5206\u6790\u306e\u7406\u8ad6\u3092\u7c21\u5358\u306b\u7d39\u4ecb\u3057\u305f\u5f8c\u3001Python\u3067\u5b9f\u969b\u306b\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u6240\u307e\u3067\u3084\u3063\u3066\u307f\u307e\u3059\u3002<\/p>\n\n\n\n\n\n\n

\u7c21\u5358\u306b\u7406\u8ad6\u7d39\u4ecb<\/h2>\n\n\n\n

\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u5206\u6790\u306f(\u91cd)\u56de\u5e30\u5206\u6790<\/a>\u3092\u5fdc\u7528\u3057\u305f\u624b\u6cd5\u306e\u3072\u3068\u3064\u3067\u3059\u3002<\/p>\n\n\n\n

\nhttps:\/\/analysis-navi.com\/?p=1932\n<\/div><\/figure>\n\n\n\n

\u56de\u5e30\u5206\u6790\u3084\u91cd\u56de\u5e30\u5206\u6790\u3067\u5206\u304b\u308b\u306e\u306f\u57fa\u672c\u7684\u306b\u300c\u76f4\u7dda\u306e\u95a2\u4fc2\u300d<\/strong>\u3060\u3051\u3067\u3057\u305f\u3002
\u3064\u307e\u308a\u3001\\(y=a_1 x_1+a_2 x_2+a_3 x_3+…+a_n x_n+b\\)\u30fb\u30fb\u30fb\u3068\u3044\u30461\u6b21\u95a2\u6570\u306e\u95a2\u4fc2\u3067\u3059\u3002
x\u3092\u52d5\u304b\u305b\u3070\u3001y\u306f\u3069\u3093\u306a\u5024\u3082\u53d6\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n

\u3057\u304b\u3057\u3001\u4eca\u77e5\u308a\u305f\u3044\u306e\u306f\u3001\u300c\u30e6\u30fc\u30b6\u767b\u9332\u3059\u308b\u53ef\u80fd\u6027\u306f\u4f55%\u306a\u306e\u304b\u300d<\/strong>\u3067\u3059\u3002\u3088\u3063\u3066\u3001x\u306f\u5e7e\u3064\u3067\u3082y\u306f0\u301c1\u306b\u53ce\u307e\u3063\u3066\u3044\u3066\u6b32\u3057\u3044<\/span><\/strong>\u3067\u3059\u306d\u3002<\/p>\n\n\n\n

\u305d\u3053\u3067\u3001\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u5206\u6790\u3067\u306f\u3001\u4e0a\u8a18\u306e\uff11\u6b21\u95a2\u6570\u306e\u5f0f\u3092\u4e0b\u8a18\u306e\u3088\u3046\u306b\u5c11\u3057\u3044\u3058\u308a\u307e\u3059\u3002
\\[y=\\frac{1}{1+e^{-(a_1 x_1+a_2 x_2+a_3 x_3+…+a_n x_n+b)}}\\]<\/p>\n\n\n\n

\u3053\u308c\u304c\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u5206\u6790\u3067\u7528\u3044\u308b\u95a2\u6570\u3067\u3059\u3002\u3053\u306e\u5f0f\u306fx\u306b\u3069\u3093\u306a\u5024\u304c\u5165\u3063\u3066\u3082y\u306f0\u301c1\u306e\u5024\u3092\u53d6\u308a\u307e\u3059\u3002
\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u5206\u6790\u306f\u5358\u306b\u300c\u3053\u306e\u5f0f\u3092\u4f7f\u3063\u3066\u56de\u5e30\u5206\u6790\u3059\u308b\u300d\u3068\u3044\u3046\u3060\u3051\u3067\u3001\u307b\u307c\u901a\u5e38\u306e\u56de\u5e30\u5206\u6790\u3068\u4e00\u7dd2<\/span><\/strong>\u3067\u3059\u3002<\/p>\n\n\n\n

Python\u3067\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u5206\u6790\u3092\u884c\u3046<\/h2>\n\n\n\n

\u3067\u306f\u3001\u4eca\u56de\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u30c7\u30fc\u30bf(user_data.csv)\u3092\u7528\u3044\u3066\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u5206\u6790\u3092\u884c\u3063\u3066\u307f\u307e\u3059\u3002
\"\"<\/p>\n\n\n\n

\u3053\u306e\u3088\u3046\u306b\u8981\u56e0\u30c7\u30fc\u30bf\u306b\u306f\u3001\u300c\u6027\u5225\u300d\u306e\u3088\u3046\u306a\u30ab\u30c6\u30b4\u30ea\u30fc\u60c5\u5831\u3060\u3051\u3067\u306a\u304f\u3001\u300c\u6ede\u5728\u6642\u9593\u300d\u306e\u3088\u3046\u306a\u6570\u5024\u60c5\u5831\u3082\u542b\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/strong><\/p>\n\n\n\n

\u3053\u306e\u30c7\u30fc\u30bf\u3092\u7528\u3044\u3066\u65e9\u901f\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u5206\u6790\u3092\u884c\u3063\u3066\u3044\u304f\u306e\u3067\u3059\u304c\u3001statmodels<\/strong>\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u3048\u3070\u901a\u5e38\u306e\u56de\u5e30\u5206\u6790<\/a>\u3068\u307b\u307c\u540c\u69d8\u306e\u66f8\u304d\u65b9\u3067\u5b9f\u884c\u3067\u304d\u308b\u306e\u3067\u3001\u4e00\u6c17\u306b\u884c\u304d\u307e\u3059\u3002<\/p>\n\n\n\n

import numpy as np\nimport pandas as pd\n\n#CSV\u8aad\u307f\u8fbc\u307f\ndf = pd.read_csv(\"user_data.csv\")\n\n#\u76ee\u7684\u5909\u6570\u540d\u306e\u6307\u5b9a\ny_name = \"\u30e6\u30fc\u30b6\u767b\u9332\"\n\n#\u5f93\u5c5e\u5909\u6570\uff08\u4f7f\u7528\u5217\uff09\u306e\u9078\u629e\nX_name = [\"\u6027\u5225\", \"\u5b66\u751f\",\"\u6ede\u5728\u6642\u9593(\u79d2)\"]\n\n#X\u3068y\u306b\u5206\u96e2\nX = df[X_name]\ny = df[y_name]\n\n#\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u5206\u6790\nfrom sklearn.linear_model import LogisticRegression\nimport statsmodels.api as sm\n\nmodel = sm.Logit(y, sm.add_constant(X))\nresult = model.fit(disp=0)\nprint(result.summary())<\/pre>\n\n\n\n
\u4e0a\u8a18\u3092\u5b9f\u884c\u3059\u308b\u3068\u30fb\u30fb\u30fb<\/p>\n\n\n\n
                           Logit Regression Results                           \n==============================================================================\nDep. Variable:                  \u30e6\u30fc\u30b6\u767b\u9332   No. Observations:                   40\nModel:                          Logit   Df Residuals:                       36\nMethod:                           MLE   Df Model:                            3\nDate:                Thu, 25 Jul 2019   Pseudo R-squ.:                  0.2588\nTime:                        03:58:19   Log-Likelihood:                -19.615\nconverged:                       True   LL-Null:                       -26.463\n                                        LLR p-value:                  0.003350\n==============================================================================\n                 coef    std err          z      P>|z|      [0.025      0.975]\n------------------------------------------------------------------------------\nconst         -4.2982      1.555     -2.764      0.006      -7.346      -1.250\n\u6027\u5225             0.5251      0.842      0.624      0.533      -1.124       2.174\n\u5b66\u751f             2.0147      0.841      2.396      0.017       0.367       3.663\n\u6ede\u5728\u6642\u9593(\u79d2)        0.0390      0.019      2.087      0.037       0.002       0.076\n==============================================================================<\/pre>\n\n\n\n
\u203b\u8868\u306e\u898b\u65b9\u7b49\u306b\u3064\u3044\u3066\u306f\u91cd\u56de\u5e30\u5206\u6790<\/a>\u306e\u30da\u30fc\u30b8\u306b\u3066\u3002<\/p>\n\n\n\n
\u3061\u306a\u307f\u306b\u3001\u76f4\u7dda\u95a2\u4fc2\u306e\u56de\u5e30\u5206\u6790\u4ee5\u5916\u306b\u306f\u4e00\u822c\u7684\u306bR2<\/sup>\u5024\u306f\u8a08\u7b97\u3055\u308c\u307e\u305b\u3093\u3002<\/span><\/strong>
\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u5206\u6790\u306e\u5834\u5408\u306f\u3001\u305d\u306e\u4ee3\u7528\u3067\u3042\u308b\u300cPseudo R-squ.\u300d\u3092\u898b\u308b\u304b\u3001p\u5024\u304b\u3089\u7cbe\u5ea6\u3092\u5224\u65ad\u3057\u3066\u3044\u304f\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
\u3053\u3053\u3067p\u5024\u3092\u898b\u3066\u307f\u308b\u3068\u3001\u30e6\u30fc\u30b6\u767b\u9332\u3057\u3066\u304f\u308c\u308b\u304b\u3069\u3046\u304b\u306b\u306f\u3001\u300c\u5b66\u751f\u304b\u3069\u3046\u304b\u300d\u3068\u300c\u6ede\u5728\u6642\u9593\u306e\u9577\u3055\u300d\u304c\u91cd\u8981<\/span><\/strong>\u306a\u3088\u3046\u3067\u3059\u3002
\u9006\u306b\u3001\u6027\u5225\u306f\u6b86\u3069\u95a2\u4fc2\u306a\u3055\u305d\u3046\u3067\u3059\u3002\u3068\u3044\u3046\u3053\u3068\u3067\u3001\u6027\u5225\u306f\u629c\u3044\u3066\u3082\u3046\u4e00\u56de\u5b9f\u884c\u3057\u3066\u307f\u307e\u3059\u3002<\/p>\n\n\n\n
X_name = [\"\u6027\u5225\", \"\u5b66\u751f\",\"\u6ede\u5728\u6642\u9593(\u79d2)\"]<\/code>
\u3092\u3001
X_name = [\"\u5b66\u751f\",\"\u6ede\u5728\u6642\u9593(\u79d2)\"]<\/code>
\u306b\u3059\u308b\u3060\u3051\u3067OK\u3067\u3059\u3002
\u3059\u308b\u3068\u30fb\u30fb\u30fb\u3002<\/p>\n\n\n\n
                           Logit Regression Results                           \n==============================================================================\nDep. Variable:                  \u30e6\u30fc\u30b6\u767b\u9332   No. Observations:                   40\nModel:                          Logit   Df Residuals:                       37\nMethod:                           MLE   Df Model:                            2\nDate:                Sat, 27 Jul 2019   Pseudo R-squ.:                  0.2513\nTime:                        14:44:11   Log-Likelihood:                -19.813\nconverged:                       True   LL-Null:                       -26.463\n                                        LLR p-value:                  0.001295\n==============================================================================\n                 coef    std err          z      P>|z|      [0.025      0.975]\n------------------------------------------------------------------------------\nconst         -4.1094      1.507     -2.728      0.006      -7.062      -1.157\n\u5b66\u751f             1.8831      0.798      2.361      0.018       0.320       3.447\n\u6ede\u5728\u6642\u9593(\u79d2)        0.0403      0.018      2.188      0.029       0.004       0.076\n==============================================================================<\/pre>\n\n\n\n
\u3059\u3079\u3066\u306ep\u5024\u304c\u4f4e\u304f\u306a\u308a\u3001\u826f\u3044\u63a8\u5b9a\u306b\u306a\u3063\u3066\u3044\u305d\u3046\u3067\u3059\u306d\u3002
\u3053\u308c\u3088\u308a\u3001\u3092\u300c\u30e6\u30fc\u30b6\u767b\u9332\u3059\u308b\u53ef\u80fd\u6027\u300d\u3092\\(y\\)\u3001\u300c\u5b66\u751f\u304b\u3069\u3046\u304b\u300d\u3092\\(x_1\\)\u3001\u300c\u6ede\u5728\u6642\u9593(\u79d2)\u300d\u3092\\(x_2\\)\u3068\u3059\u308b\u3068\u3001<\/p>\n\n\n\n
\\[y=\\frac{1}{1+e^{-(1.8831 x_1+0.0403 x_2-4.1094)}}\\]
\u3068\u4e88\u6e2c\u3067\u304d\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
\u672a\u77e5\u30c7\u30fc\u30bf\u306e\u63a8\u6e2c<\/h2>\n\n\n\n
\u3069\u306e\u30e6\u30fc\u30b6\u767b\u9332\u3057\u3066\u304f\u308c\u308b\u4eba\u306e\u50be\u5411\u306f\u5206\u304b\u308a\u307e\u3057\u305f\u304c\u3001\u304a\u6b21\u306f\u672a\u77e5\u30c7\u30fc\u30bf\u306e\u4e88\u6e2c\u3082\u3084\u3063\u3066\u307f\u307e\u3059\u3002
\u3064\u307e\u308a\u3001\u300c\u5b66\u751f\u304b\u3069\u3046\u304b\u300d\u300c\u6ede\u5728\u6642\u9593\u300d\u306e2\u3064\u306e\u60c5\u5831\u304b\u3089\u3001\u305d\u306e\u4eba\u304c\u30e6\u30fc\u30b6\u767b\u9332\u3057\u3066\u304f\u308c\u308b\u78ba\u7387\u3092\u6c42\u3081\u308b<\/span><\/strong>\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
\u4e88\u6e2c\u306b\u4f7f\u3046\u30c7\u30fc\u30bf(user_data_future.csv)\u306f\u4ee5\u4e0b\u3002
\"\"<\/p>\n\n\n\n
\u3053\u3061\u3089\u3082\u3001\u307b\u307c\u901a\u5e38\u306e\u91cd\u56de\u5e30\u5206\u6790<\/a>\u3068\u540c\u69d8\u306e\u3084\u308a\u65b9\u3068\u306a\u308a\u307e\u3059\u304c\u3001\u5148\u306b\u8ff0\u3079\u305f\u3068\u304a\u308a\u56de\u5e30\u5206\u6790\u306b\u4f7f\u3046\u5f0f\u304c\u9055\u3046\u306e\u3067\u3061\u3087\u3063\u3068\u3060\u3051\u8a08\u7b97\u5f0f\u3082\u5909\u308f\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
###\u672a\u77e5\u30c7\u30fc\u30bf\u306e\u63a8\u6e2c\ndf_test = pd.read_csv(\"user_data_future.csv\")\nimport math\ny_result=[]\nfor i in range(len(df_test.index)):\n    y_tmp = result.params.const\n    for j in range(len(X_name)):\n        x_name = X_name[j]\n        y_tmp += result.params[x_name] * df_test[x_name][i]\n    y_result.append(1 \/ (1 + math.e**-y_tmp))\nprint(y_result)<\/pre>\n\n\n\n
\u6700\u5f8c\u306be\u306e\u8a08\u7b97\u304c\u5165\u308b\u306e\u3067\u3001math\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u547c\u3073\u51fa\u3057\u3066\u3044\u307e\u3059\u3002
\u4e0a\u8a18\u3092\u5b9f\u884c\u3059\u308b\u3068\u30fb\u30fb\u3002<\/p>\n\n\n\n
[0.6170412161588048, 0.2928466124681998, 0.03413222194098345, 0.8154306982851297, 0.5082962248385622, 0.08205130708481542, 0.4612411916120478, 0.7152109821321985, 0.38269487197510876, 0.6074631589429199]<\/pre>\n\n\n\n
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\u3059\u308bor\u3057\u306a\u3044\u3092\u306f\u3063\u304d\u308a\u3055\u305b\u305f\u3044\u5834\u5408\u306f\u3001\u300c0.5\u4ee5\u4e0a\u306a\u3089\u30e6\u30fc\u30b6\u767b\u9332\u3059\u308b\u30010.5\u672a\u6e80\u306a\u3089\u3057\u306a\u3044\u300d<\/span><\/strong>\u3068\u8003\u3048\u3066\u826f\u3044\u3067\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
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