{"id":19437,"date":"2017-12-11T12:15:19","date_gmt":"2017-12-11T10:15:19","guid":{"rendered":"https:\/\/www.webcodegeeks.com\/?p=19437"},"modified":"2017-12-06T10:50:32","modified_gmt":"2017-12-06T08:50:32","slug":"scikit-learn-building-multi-class-classification-ensemble","status":"publish","type":"post","link":"https:\/\/www.webcodegeeks.com\/python\/scikit-learn-building-multi-class-classification-ensemble\/","title":{"rendered":"scikit-learn: Building a multi class classification ensemble"},"content":{"rendered":"<p>For the Kaggle <a href=\"https:\/\/www.kaggle.com\/c\/spooky-author-identification\">Spooky Author Identification<\/a> I wanted to combine multiple classifiers together into an ensemble and found the <a href=\"http:\/\/scikit-learn.org\/stable\/modules\/ensemble.html#voting-classifier\"><cite>VotingClassifier<\/cite><\/a> that does exactly that.<\/p>\n<p>We need to predict the probability that a sentence is written by one of three authors so the VotingClassifier needs to make a \u2018soft\u2019 prediction. If we only needed to know the most likely author we could have it make a \u2018hard\u2019 prediction instead.<\/p>\n<p>We start with three classifiers which generate different n-gram based features. The code for those is as follows:<\/p>\n<pre class=\"brush:py\">from sklearn import linear_model\r\nfrom sklearn.ensemble import VotingClassifier\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\nfrom sklearn.naive_bayes import MultinomialNB\r\nfrom sklearn.pipeline import Pipeline\r\n\u00a0\r\nngram_pipe = Pipeline([\r\n    ('cv', CountVectorizer(ngram_range=(1, 2))),\r\n    ('mnb', MultinomialNB())\r\n])\r\n\u00a0\r\nunigram_log_pipe = Pipeline([\r\n    ('cv', CountVectorizer()),\r\n    ('logreg', linear_model.LogisticRegression())\r\n])<\/pre>\n<p>We can combine those classifiers together like this:<\/p>\n<pre class=\"brush:py\">classifiers = [\r\n    (\"ngram\", ngram_pipe),\r\n    (\"unigram\", unigram_log_pipe),\r\n]\r\n\u00a0\r\nmixed_pipe = Pipeline([\r\n    (\"voting\", VotingClassifier(classifiers, voting=\"soft\"))\r\n])<\/pre>\n<p>Now it\u2019s time to test our ensemble. I got the code for the test function from <a href=\"https:\/\/www.kaggle.com\/sohier\/intermediate-tutorial-python\/\">Sohier Dane<\/a>\u2018s tutorial.<\/p>\n<pre class=\"brush:py\">import pandas as pd\r\nimport numpy as np\r\n\u00a0\r\nfrom sklearn.model_selection import StratifiedKFold\r\nfrom sklearn import metrics\r\n\u00a0\r\nY_COLUMN = \"author\"\r\nTEXT_COLUMN = \"text\"\r\n\u00a0\r\n\u00a0\r\ndef test_pipeline(df, nlp_pipeline):\r\n    y = df[Y_COLUMN].copy()\r\n    X = pd.Series(df[TEXT_COLUMN])\r\n    rskf = StratifiedKFold(n_splits=5, random_state=1)\r\n    losses = []\r\n    accuracies = []\r\n    for train_index, test_index in rskf.split(X, y):\r\n        X_train, X_test = X[train_index], X[test_index]\r\n        y_train, y_test = y[train_index], y[test_index]\r\n        nlp_pipeline.fit(X_train, y_train)\r\n        losses.append(metrics.log_loss(y_test, nlp_pipeline.predict_proba(X_test)))\r\n        accuracies.append(metrics.accuracy_score(y_test, nlp_pipeline.predict(X_test)))\r\n\u00a0\r\n    print(\"{kfolds log losses: {0}, mean log loss: {1}, mean accuracy: {2}\".format(\r\n        str([str(round(x, 3)) for x in sorted(losses)]),\r\n        round(np.mean(losses), 3),\r\n        round(np.mean(accuracies), 3)\r\n    ))\r\n\u00a0\r\ntrain_df = pd.read_csv(\"train.csv\", usecols=[Y_COLUMN, TEXT_COLUMN])\r\ntest_pipeline(train_df, mixed_pipe)<\/pre>\n<p>Let\u2019s run <a href=\"https:\/\/gist.github.com\/mneedham\/0f640497ae3c662fc89fda199b5b7833\">the script<\/a>:<\/p>\n<pre class=\"brush:text\">kfolds log losses: ['0.388', '0.391', '0.392', '0.397', '0.398'], mean log loss: 0.393 mean accuracy: 0.849<\/pre>\n<p>Looks good.<\/p>\n<p>I\u2019ve actually got several other classifiers as well but I\u2019m not sure which ones should be part of the ensemble. In a future post we\u2019ll look at how to use <a href=\"http:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.model_selection.GridSearchCV.html\">GridSearch<\/a> to work that out.<\/p>\n<div class=\"attribution\">\n<table>\n<tbody>\n<tr>\n<td>Published on Web Code Geeks with permission by Mark Needham, partner at our <a href=\"http:\/\/www.webcodegeeks.com\/join-us\/wcg\/\" target=\"_blank\" rel=\"noopener\">WCG program<\/a>. See the original article here: <a href=\"http:\/\/www.markhneedham.com\/blog\/2017\/12\/05\/scikit-learn-building-multi-class-classification-ensemble\/\" target=\"_blank\" rel=\"noopener\">scikit-learn: Building a multi class classification ensemble<\/a><\/p>\n<p>Opinions expressed by Web Code Geeks contributors are their own.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>For the Kaggle Spooky Author Identification I wanted to combine multiple classifiers together into an ensemble and found the VotingClassifier that does exactly that. We need to predict the probability that a sentence is written by one of three authors so the VotingClassifier needs to make a \u2018soft\u2019 prediction. If we only needed to know &hellip;<\/p>\n","protected":false},"author":48,"featured_media":1651,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[53],"tags":[],"class_list":["post-19437","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>scikit-learn: Building a multi class classification ensemble - Web Code Geeks - 2026<\/title>\n<meta name=\"description\" content=\"For the Kaggle Spooky Author Identification I wanted to combine multiple classifiers together into an ensemble and found the VotingClassifier that does\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.webcodegeeks.com\/python\/scikit-learn-building-multi-class-classification-ensemble\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"scikit-learn: Building a multi class classification ensemble - 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