在java代码中使用mahout，而不是cli

Question

我希望能够使用 Java 构建模型，我可以使用 CLI 作为 folowing：

    ./mahout trainlogistic --input Candy-Crush.twtr.csv 
       --output ./model 
       --target hd_click --categories 2 
       --predictors click_frequency country_code ctr      device_price_range hd_conversion  time_of_day num_clicks phone_type twitter is_weekend app_entertainment app_wallpaper app_widgets arcade books_and_reference brain business cards casual comics communication education entertainment finance game_wallpaper game_widgets health_and_fitness health_fitness libraries_and_demo libraries_demo lifestyle media_and_video media_video medical music_and_audio news_and_magazines news_magazines personalization photography productivity racing shopping social sports sports_apps sports_games tools transportation travel_and_local weather app_entertainment_percentage app_wallpaper_percentage app_widgets_percentage arcade_percentage books_and_reference_percentage brain_percentage business_percentage cards_percentage casual_percentage comics_percentage communication_percentage education_percentage entertainment_percentage finance_percentage game_wallpaper_percentage game_widgets_percentage health_and_fitness_percentage health_fitness_percentage libraries_and_demo_percentage libraries_demo_percentage lifestyle_percentage media_and_video_percentage media_video_percentage medical_percentage music_and_audio_percentage news_and_magazines_percentage news_magazines_percentage personalization_percentage photography_percentage productivity_percentage racing_percentage shopping_percentage social_percentage sports_apps_percentage sports_games_percentage sports_percentage tools_percentage transportation_percentage travel_and_local_percentage weather_percentage reads_magazine_sum reads_magazine_count interested_in_gardening_sum interested_in_gardening_count kids_birthday_coming_sum kids_birthday_coming_count job_seeker_sum job_seeker_count friends_sum friends_count married_sum married_count charity_donor_sum charity_donor_count student_sum student_count interested_in_real_estate_sum interested_in_real_estate_count sports_fan_sum sports_fan_count bascketball_sum bascketball_count interested_in_politics_sum interested_in_politics_count gamer_sum gamer_count activist_sum activist_count traveler_sum traveler_count likes_soccer_sum likes_soccer_count interested_in_celebs_sum interested_in_celebs_count auto_racing_sum auto_racing_count age_group_sum age_group_count healthy_lifestyle_sum healthy_lifestyle_count interested_in_finance_sum interested_in_finance_count sports_teams_usa_sum sports_teams_usa_count interested_in_deals_sum interested_in_deals_count business_oriented_sum business_oriented_count interested_in_cooking_sum interested_in_cooking_count music_lover_sum music_lover_count beauty_sum beauty_count follows_fashion_sum follows_fashion_count likes_wrestling_sum likes_wrestling_count name_sum name_count shopper_sum shopper_count golf_sum golf_count vegetarian_sum vegetarian_count dating_sum dating_count interested_in_fashion_sum interested_in_fashion_count interested_in_news_sum interested_in_news_count likes_tennis_sum likes_tennis_count male_sum male_count interested_in_cars_sum interested_in_cars_count follows_bloggers_sum follows_bloggers_count entertainment_sum entertainment_count interested_in_books_sum interested_in_books_count has_kids_sum has_kids_count interested_in_movies_sum interested_in_movies_count musicians_sum musicians_count tech_oriented_sum tech_oriented_count female_sum female_count has_pet_sum has_pet_count practicing_sports_sum practicing_sports_count 
       --types      numeric         word         numeric  word               word           word        numeric    word       word    word        numeric       
       --features 100 --passes 1 --rate 50

我无法理解 20 个新闻组的例子，因为它需要学习。任何人都可以给我一个与 CLI 命令相同的代码吗？

澄清一下：

我需要这样的东西：

    model.train(1,0,"monday",6,44,1,7,4,6,78,7,3,4,6,........,"good");
    model.train(1,0,"sunday",6,44,5,7,9,2,4,6,78,7,3,4,6,........,"bad");
    model.train(1,0,"monday",4,99,2,4,6,3,4,6,........,"good");
    model.writeTofile("myModel.model");

如果您不熟悉分类，只想告诉我如何从 JAVA 执行 CLI 命令，请不要回答

Answer 1

我不是 100% 熟悉 Mahout API(我同意文档非常稀疏(，所以我只能给出指针，但我希望它有所帮助：

trainlogistic示例的 Java 源代码实际上可以在mahout-examples库中找到 - 它在 maven [0] (org.apache.mahout.classifier.sgd.TrainLogistic 中(。我想如果你愿意，你可以使用完全相同的源代码，但它取决于mahout-examples库中的几个实用程序类(而且也不是很干净(。

在此示例中执行训练的类org.apache.mahout.classifier.sgd.OnlineLogisticRegression [1]，尽管考虑到您拥有的大量预测变量，您可能希望使用AdaptiveLogisticRegression [2](相同的包(，它在内部使用许多OnlineLogisticRegression。但是您必须亲自查看哪种数据最适合您的数据。

API 相当简单，有一个train方法，它Vector输入数据，一个classify方法来测试你的模型，以及learningRate和其他方法来更改模型的参数。

要像命令行工具一样将模型保存到磁盘，请使用 org.apache.mahout.classifier.sgd.ModelSerializer ，它具有一个简单的 API 来写入和读取模型。(OLR 类本身也有write和readFields方法，但坦率地说，我不确定它们的作用，或者是否有区别ModelSerializer - 它们也没有记录在案。

最后，除了mahout-examples中的源代码之外，这里还有两个直接使用Mahout API的例子，可能很有用[3,4]。

来源：

[0] http://repo1.maven.org/maven2/org/apache/mahout/mahout-examples/0.8/

[1] http://archive.cloudera.com/cdh4/cdh/4/mahout/mahout-core/org/apache/mahout/classifier/sgd/OnlineLogisticRegression.html

[2] http://archive.cloudera.com/cdh4/cdh/4/mahout/mahout-core/org/apache/mahout/classifier/sgd/AdaptiveLogisticRegression.html

[3] http://mail-archives.apache.org/mod_mbox/mahout-user/201206.mbox/[e-mail]%3E

[4] http://skife.org/mahout/2013/02/14/first_steps_with_mahout.html

Answer 2

这个博客有一篇关于如何使用Mahout Java API进行训练和分类的好文章： http://nigap.blogspot.com/2012/02/bayes-algorithm-with-apache-mahout.html

Answer 3

您可以使用 Runtime.exec 从 java 执行相同的 cmd 行。

简单的方法是：

Process p = Runtime.getRuntime().exec("/usr/bin/bash -ic "<path_to_mahout>/mahout trainlogistic --input Candy-Crush.twtr.csv " + "--output ./model " + "--target hd_click --categories 2 " + "--predictors click_frequency country_code ctr device_price_range hd_conversion time_of_day num_clicks phone_type twitter is_weekend app_entertainment app_wallpaper app_widgets arcade books_and_reference brain business cards casual comics communication education entertainment finance game_wallpaper game_widgets health_and_fitness health_fitness libraries_and_demo libraries_demo lifestyle media_and_video media_video medical music_and_audio news_and_magazines news_magazines personalization photography productivity racing shopping social sports sports_apps sports_games tools transportation travel_and_local weather app_entertainment_percentage app_wallpaper_percentage app_widgets_percentage arcade_percentage books_and_reference_percentage brain_percentage business_percentage cards_percentage casual_percentage comics_percentage communication_percentage education_percentage entertainment_percentage finance_percentage game_wallpaper_percentage game_widgets_percentage health_and_fitness_percentage health_fitness_percentage libraries_and_demo_percentage libraries_demo_percentage lifestyle_percentage media_and_video_percentage media_video_percentage medical_percentage music_and_audio_percentage news_and_magazines_percentage news_magazines_percentage personalization_percentage photography_percentage productivity_percentage racing_percentage shopping_percentage social_percentage sports_apps_percentage sports_games_percentage sports_percentage tools_percentage transportation_percentage travel_and_local_percentage weather_percentage reads_magazine_sum reads_magazine_count interested_in_gardening_sum interested_in_gardening_count kids_birthday_coming_sum kids_birthday_coming_count job_seeker_sum job_seeker_count friends_sum friends_count married_sum married_count charity_donor_sum charity_donor_count student_sum student_count interested_in_real_estate_sum interested_in_real_estate_count sports_fan_sum sports_fan_count bascketball_sum bascketball_count interested_in_politics_sum interested_in_politics_count gamer_sum gamer_count activist_sum activist_count traveler_sum traveler_count likes_soccer_sum likes_soccer_count interested_in_celebs_sum interested_in_celebs_count auto_racing_sum auto_racing_count age_group_sum age_group_count healthy_lifestyle_sum healthy_lifestyle_count interested_in_finance_sum interested_in_finance_count sports_teams_usa_sum sports_teams_usa_count interested_in_deals_sum interested_in_deals_count business_oriented_sum business_oriented_count interested_in_cooking_sum interested_in_cooking_count music_lover_sum music_lover_count beauty_sum beauty_count follows_fashion_sum follows_fashion_count likes_wrestling_sum likes_wrestling_count name_sum name_count shopper_sum shopper_count golf_sum golf_count vegetarian_sum vegetarian_count dating_sum dating_count interested_in_fashion_sum interested_in_fashion_count interested_in_news_sum interested_in_news_count likes_tennis_sum likes_tennis_count male_sum male_count interested_in_cars_sum interested_in_cars_count follows_bloggers_sum follows_bloggers_count entertainment_sum entertainment_count interested_in_books_sum interested_in_books_count has_kids_sum has_kids_count interested_in_movies_sum interested_in_movies_count musicians_sum musicians_count tech_oriented_sum tech_oriented_count female_sum female_count has_pet_sum has_pet_count practicing_sports_sum practicing_sports_count " + "--types numeric word numeric word word word numeric word word word numeric " + "--features 100 --passes 1 --rate 50"");

如果您选择这个，那么我建议先阅读以下内容：当 Runtime.exec(( 不会

这样，应用程序将在不同的进程中运行。

此外，您可以按照以下站点中的"与您的应用程序集成"部分进行操作：推荐器文档

这也是编写推荐者的良好参考：介绍 Apache Mahout

希望这有帮助。干杯