{"id":1046,"date":"2021-03-23T20:37:26","date_gmt":"2021-03-23T11:37:26","guid":{"rendered":"https:\/\/technologyagency.net\/wordpress\/?p=1046"},"modified":"2021-03-27T15:15:13","modified_gmt":"2021-03-27T06:15:13","slug":"%e8%a4%87%e6%95%b0%e3%81%ae%e6%a9%9f%e6%a2%b0%e5%ad%a6%e7%bf%92%e3%82%a2%e3%83%ab%e3%82%b4%e3%83%aa%e3%82%ba%e3%83%a0%e3%82%92%e6%af%94%e8%bc%83%e3%81%99%e3%82%8b","status":"publish","type":"post","link":"https:\/\/technologyagency.net\/wordpress\/2021\/03\/23\/%e8%a4%87%e6%95%b0%e3%81%ae%e6%a9%9f%e6%a2%b0%e5%ad%a6%e7%bf%92%e3%82%a2%e3%83%ab%e3%82%b4%e3%83%aa%e3%82%ba%e3%83%a0%e3%82%92%e6%af%94%e8%bc%83%e3%81%99%e3%82%8b\/","title":{"rendered":"\u8907\u6570\u306e\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u6bd4\u8f03\u3059\u308b"},"content":{"rendered":"\n

Recommendation System\u3092\u8003\u3048\u308b \u305d\u306e\uff12<\/a>\u3067\u3001\u6a5f\u68b0\u5b66\u7fd2\u3092\u5b9f\u65bd\u3057\u305f\u7d50\u679c\u3001\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u304c\u826f\u3044\u30b9\u30b3\u30a2\u306b\u306a\u3063\u305f\u304c\u3001\u305d\u306e\u4ed6\u306e\u5206\u985e\u5668\u3067\u306f\u3069\u3046\u304b\u5927\u307e\u304b\u306b\u8abf\u3079\u3066\u307f\u308b\u3002<\/p>\n\n\n\n

\uff11\uff0e\u8907\u6570\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u6bd4\u8f03\u3059\u308b<\/h2>\n\n\n\n

\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b\u3002<\/p>\n\n\n

\n# data analysis and wrangling\nimport pandas as pd\nimport numpy as np\nimport random as rnd\n\n# visualization\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nimport seaborn as sns\ncolor = sns.color_palette()\nsns.set_style('darkgrid')\n\nimport warnings\ndef ignore_warn(*args, **kwargs):\n  pass\nwarnings.warn = ignore_warn # Ignore annoying warning (from sklearn and seaborn)\n\n# machine learning\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC, LinearSVC\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.linear_model import Perceptron, SGDClassifier\nfrom sklearn.tree import DecisionTreeClassifier\n\nfrom sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve\n<\/pre><\/div>\n\n\n
\u4fdd\u5b58\u3057\u3066\u304a\u3044\u305f\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u3080\u3002<\/p>\n\n\n
\nfrom pathlib import Path\n\nTRAIN_DATA_PATH = str(Path.home()) + '\/vscode\/syosetu\/train_data.pkl'\n\ntrain = pd.read_pickle(TRAIN_DATA_PATH)\n<\/pre><\/div>\n\n\n
\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30c7\u30fc\u30bf\u3068\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u306b\u5206\u5272\u3059\u308b\u3002<\/p>\n\n\n
\nfrom sklearn.model_selection import train_test_split\n\nX = train['vectors']\ny = train['female']\n\nX_array = np.array([np.array(v) for v in X])\ny_array = np.array([i for i in y])\n\nX_train, X_test, y_train, y_test = train_test_split(X_array, y_array, random_state=0)\n<\/pre><\/div>\n\n\n
\u30af\u30ed\u30b9\u30d0\u30ea\u30c7\u30fc\u30b7\u30e7\u30f3\u306e\u30b9\u30b3\u30a2\u304c\u826f\u3044\u9806\u306b\u8868\u793a\u3059\u308b\u3002<\/p>\n\n\n
\nkfold = StratifiedKFold(n_splits=10)\ncls_weight = (y_train.shape[0] - np.sum(y_train)) \/ np.sum(y_train)\nrandom_state = 0\nclassifiers = []\nclassifiers.append(LogisticRegression(random_state=random_state))\nclassifiers.append(SVC(random_state=random_state))\nclassifiers.append(KNeighborsClassifier(n_neighbors=3))\nclassifiers.append(GaussianNB())\nclassifiers.append(Perceptron(random_state=random_state))\nclassifiers.append(LinearSVC(random_state=random_state))\nclassifiers.append(SGDClassifier(random_state=random_state))\nclassifiers.append(DecisionTreeClassifier(random_state=random_state))\nclassifiers.append(RandomForestClassifier(n_estimators=100, random_state=random_state))\nclassifiers.append(AdaBoostClassifier(DecisionTreeClassifier(random_state=random_state), random_state=random_state, learning_rate=0.1))\nclassifiers.append(GradientBoostingClassifier(random_state=random_state))\nclassifiers.append(ExtraTreesClassifier(random_state=random_state))\n\ncv_results = []\nfor classifier in classifiers :\n  cv_results.append(cross_val_score(classifier, X_train, y=y_train, scoring='accuracy', cv=kfold, n_jobs=4))\n\ncv_means = []\ncv_std = []\nfor cv_result in cv_results:\n  cv_means.append(cv_result.mean())\n  cv_std.append(cv_result.std())\n\ncv_res = pd.DataFrame({'CrossVal Means': cv_means, 'CrossVal Errors': cv_std,\n                       'Algorithm': ['LogisticRegression', 'SVC', 'KNeighborsClassifier',\n                                     'GaussianNB', 'Perceptron', 'LinearSVC', 'SGDClassifier',\n                                     'DecisionTreeClassifier', 'RandomForestClassifier',\n                                     'AdaBoostClassifier', 'GradientBoostingClassifier',\n                                     'ExtraTreesClassifier']})\n\ncv_res.sort_values(by=['CrossVal Means'], ascending=False, inplace=True)\n\ncv_res\n<\/pre><\/div>\n\n\n
<\/th>CrossVal Means<\/th>CrossVal Errors<\/th>Algorithm<\/th><\/tr><\/thead>
0<\/th>0.905357<\/td>0.105357<\/td>LogisticRegression<\/td><\/tr>
3<\/th>0.896429<\/td>0.094761<\/td>GaussianNB<\/td><\/tr>
1<\/th>0.892857<\/td>0.100699<\/td>SVC<\/td><\/tr>
4<\/th>0.891071<\/td>0.101157<\/td>Perceptron<\/td><\/tr>
6<\/th>0.891071<\/td>0.101157<\/td>SGDClassifier<\/td><\/tr>
11<\/th>0.878571<\/td>0.099360<\/td>ExtraTreesClassifier<\/td><\/tr>
8<\/th>0.866071<\/td>0.120387<\/td>RandomForestClassifier<\/td><\/tr>
2<\/th>0.844643<\/td>0.092461<\/td>KNeighborsClassifier<\/td><\/tr>
5<\/th>0.825000<\/td>0.107381<\/td>LinearSVC<\/td><\/tr>
10<\/th>0.801786<\/td>0.112783<\/td>GradientBoostingClassifier<\/td><\/tr>
7<\/th>0.746429<\/td>0.145248<\/td>DecisionTreeClassifier<\/td><\/tr>
9<\/th>0.708929<\/td>0.121389<\/td>AdaBoostClassifier<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u56de\u5e30\u7cfb\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3067\u30b9\u30b3\u30a2\u304c\u826f\u3044\u50be\u5411\u304c\u3042\u308b\u3088\u3046\u306b\u898b\u3048\u308b\u3002<\/p>\n\n\n
\ng = sns.barplot('CrossVal Means', 'Algorithm', data=cv_res, palette='Set3', orient='h', **{'xerr': cv_std})\ng.set_xlabel('Mean Accuracy')\ng = g.set_title('Cross validation scores')\n<\/pre><\/div>\n\n\n
\"\"<\/figure>\n\n\n\n
\uff12\uff0e\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u306e\u6700\u9069\u5316<\/h2>\n\n\n\n
\u7d9a\u3044\u3066\u3001\u3044\u304f\u3064\u304b\u9078\u3093\u3060\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u3092\u5909\u66f4\u3057\u3066\u3001\u30b9\u30b3\u30a2\u306e\u5909\u5316\u3092\u898b\u3066\u307f\u308b\u3002<\/p>\n\n\n
\n### META MODELING WITH LR, RF and SGD\n\n# Logistic Regression\nLR = LogisticRegression(random_state=0)\n\n## Search grid for optimal parameters\nlr_param_grid = {'penalty': ['l1','l2']}\n\ngsLR = GridSearchCV(LR, param_grid=lr_param_grid, cv=kfold, scoring='accuracy', n_jobs=4, verbose=1)\n\ngsLR.fit(X_train, y_train)\n\nLR_best = gsLR.best_estimator_\n\n# Best score\ngsLR.best_score_\n<\/pre><\/div>\n\n\n
Fitting 10 folds for each of 2 candidates, totalling 20 fits
0.9053571428571429<\/p>\n\n\n
\ngsLR.best_params_\n<\/pre><\/div>\n\n\n
{‘penalty’: ‘l2’}<\/p>\n\n\n
\n# Random Forest Classifier\nRF = RandomForestClassifier(random_state=0)\n\n## Search grid for optimal parameters\n'''rf_param_grid = {'criterion': ['gini', 'entropy'],\n                 'max_features': [1, 3, 10],\n                 'min_samples_split': [2, 3, 10],\n                 'min_samples_leaf': [1, 3, 10],\n                 'n_estimators':[50, 100, 200]}'''\nrf_param_grid = {'criterion': ['gini', 'entropy'],\n                 'max_features': [40, 50, 60],\n                 'min_samples_split': [2, 3, 10],\n                 'min_samples_leaf': [1, 3, 10],\n                 'n_estimators':[20, 30, 40]}\n\ngsRF = GridSearchCV(RF, param_grid=rf_param_grid, cv=kfold, scoring='accuracy', n_jobs=4, verbose=1)\n\ngsRF.fit(X_train, y_train)\n\nRF_best = gsRF.best_estimator_\n\n# Best score\ngsRF.best_score_\n<\/pre><\/div>\n\n\n
Fitting 10 folds for each of 162 candidates, totalling 1620 fits
0.8946428571428573<\/p>\n\n\n
\ngsRF.best_params_\n<\/pre><\/div>\n\n\n
{‘criterion’: ‘gini’,
‘max_features’: 50,
‘min_samples_leaf’: 3,
‘min_samples_split’: 2,
‘n_estimators’: 30}<\/p>\n\n\n
\n# SGD Classifier\nSGD = SGDClassifier(random_state=0)\n\n## Search grid for optimal parameters\nsgd_param_grid = {'alpha'  : [0.0001, 0.001, 0.01, 0.1],\n                  'loss'   : ['log', 'modified_huber'],\n                  'penalty': ['l2', 'l1', 'none']}\n\ngsSGD = GridSearchCV(SGD, param_grid=sgd_param_grid, cv=kfold, scoring='accuracy', n_jobs=4, verbose=1)\n\ngsSGD.fit(X_train, y_train)\n\nSGD_best = gsSGD.best_estimator_\n\n# Best score\ngsSGD.best_score_\n<\/pre><\/div>\n\n\n
Fitting 10 folds for each of 24 candidates, totalling 240 fits
0.8660714285714285<\/p>\n\n\n
\ngsSGD.best_params_\n<\/pre><\/div>\n\n\n
{‘alpha’: 0.0001, ‘loss’: ‘modified_huber’, ‘penalty’: ‘l1’}<\/p>\n\n\n\n
\uff13\uff0e\u5b66\u7fd2\u66f2\u7dda\u306e\u78ba\u8a8d<\/h2>\n\n\n
\ndef plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n                        n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5)):\n  '''Generate a simple plot of the test and training learning curve'''\n  plt.figure()\n  plt.title(title)\n  if ylim is not None:\n    plt.ylim(*ylim)\n  plt.xlabel('Training examples')\n  plt.ylabel('Score')\n  train_sizes, train_scores, test_scores = learning_curve(\n      estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n  train_scores_mean = np.mean(train_scores, axis=1)\n  train_scores_std = np.std(train_scores, axis=1)\n  test_scores_mean = np.mean(test_scores, axis=1)\n  test_scores_std = np.std(test_scores, axis=1)\n  plt.grid()\n\n  plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n                   train_scores_mean + train_scores_std, alpha=0.1,\n                   color='r')\n  plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n                   test_scores_mean + test_scores_std, alpha=0.1,\n                   color='g')\n  plt.plot(train_sizes, train_scores_mean, 'o-', color='r',\n           label='Training score')\n  plt.plot(train_sizes, test_scores_mean, 'o-', color='g',\n           label='Cross-validation score')\n\n  plt.legend(loc='best')\n  return plt\n\ng = plot_learning_curve(gsLR.best_estimator_, 'LR mearning curves', X_train, y_train, cv=kfold)\ng = plot_learning_curve(gsRF.best_estimator_, 'RF learning curves', X_train, y_train, cv=kfold)\ng = plot_learning_curve(gsSGD.best_estimator_, 'SGD learning curves', X_train, y_train, cv=kfold)\n<\/pre><\/div>\n\n\n
\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Random Forest\u3082\u826f\u3044\u6c17\u304c\u3057\u3066\u304d\u305f\u3002\u4f55\u3068\u304b\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30c7\u30fc\u30bf\u3092\u5897\u3084\u3057\u3066\u3082\u3046\u5c11\u3057\u78ba\u8a8d\u3057\u305f\u3044\u3002<\/p>\n\n\n\n
\u6700\u5f8c\u306b<\/h2>\n\n\n\n
\u5168\u304f\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3067\u4f7f\u7528\u3057\u3066\u3044\u306a\u3044\u672a\u77e5\u306e\u30c7\u30fc\u30bf\u306b\u5bfe\u3057\u3066\u3001 Random Forest \u306e\u4e88\u6e2c\u30e2\u30c7\u30eb\u3067\u5224\u5b9a\u3057\u3066\u307f\u305f\u3068\u3053\u308d83\uff05\u7a0b\u306e\u6b63\u89e3\u7387\u3060\u3063\u305f\u3002\u306a\u308d\u3046\u5c0f\u8aac\u306eTop300\u306f\u30d5\u30a1\u30f3\u30bf\u30b8\u30fc\u7cfb\u306e\u5c0f\u8aac\u304c\u591a\u3044\u305f\u3081\u3001\u30d5\u30a1\u30f3\u30bf\u30b8\u30fc\u7cfb\u306e\u5c0f\u8aac\u306b\u304a\u3044\u3066\u3001\u3053\u306e\u6b63\u89e3\u7387\u3068\u307f\u305f\u65b9\u304c\u826f\u3044\u304b\u3082\u77e5\u308c\u306a\u3044\u3002\u30b8\u30e3\u30f3\u30eb\u304c\u9055\u3046\u5c0f\u8aac\u306e\u5834\u5408\u3069\u306e\u3088\u3046\u306a\u7d50\u679c\u306b\u306a\u308b\u304b\u73fe\u6642\u70b9\u3067\u306f\u4e88\u6e2c\u4e0d\u80fd\u3067\u3042\u308b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
Recommendation System\u3092\u8003\u3048\u308b \u305d\u306e\uff12\u3067\u3001\u6a5f\u68b0\u5b66\u7fd2\u3092\u5b9f\u65bd\u3057\u305f\u7d50\u679c\u3001\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u304c\u826f\u3044\u30b9\u30b3\u30a2\u306b\u306a\u3063\u305f\u304c\u3001\u305d\u306e\u4ed6\u306e\u5206\u985e\u5668\u3067\u306f\u3069\u3046\u304b\u5927\u307e\u304b\u306b\u8abf\u3079\u3066\u307f\u308b\u3002 \uff11\uff0e\u8907\u6570\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u6bd4\u8f03\u3059\u308b \u30e9\u30a4\u30d6\u30e9\u30ea\u3092 … “\u8907\u6570\u306e\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u6bd4\u8f03\u3059\u308b” \u306e<\/span>\u7d9a\u304d\u3092\u8aad\u3080<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[41],"tags":[46,51],"class_list":["post-1046","post","type-post","status-publish","format-standard","hentry","category-python","tag-46","tag-51"],"yoast_head":"\n\u8907\u6570\u306e\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u6bd4\u8f03\u3059\u308b - I want to lead an easy life.<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/technologyagency.net\/wordpress\/2021\/03\/23\/\u8907\u6570\u306e\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u6bd4\u8f03\u3059\u308b\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta 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