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在這個系列的文章裡，我們將使用 Apache Spark、XGBoost、Elasticsearch 和 MySQL 等工具來搭建一個推薦系統的 Machine Learning Pipeline。推薦系統的組成可以粗略地分成 Candidate Generation 和 Ranking 兩個部分，前者是針對用戶產生候選物品集，常用的方法有 Collaborative Filtering、Content-based、標籤配對、熱門排行或人工精選等；後者則是對這些候選物品排序，以 Top N 的方式呈現最終的推薦結果，常用的方法有 Logistic Regression。

在本篇文章中，我們將以 Ranking 階段常用的方法之一：Logistic Regression 邏輯迴歸為例，利用 Apache Spark 的 Logistic Regression 模型建立一個 GitHub repositories 的推薦系統，以用戶對 repo 的打星紀錄和用戶與 repo 的各項屬性做為特徵，預測出用戶會不會打星某個 repo（分類問題）。最後訓練出來的模型就可以做為我們的推薦系統的 Ranking 模組。不過因為 LR 是線性模型，所以通常需要大量的 Feature Engineering 來習得非線性關係。所以這篇文章的重點是 Spark ML 的 Pipeline 機制和特徵工程，不會在演算法的部分著墨太多。

完整的程式碼可以在 https://github.com/vinta/albedo 找到。

系列文章：

• Build a recommender system with Spark: Implicit ALS
• Build a recommender system with Spark: Content-based and Elasticsearch
• Build a recommender system with Spark: Logistic Regression
• Feature Engineering 特徵工程中常見的方法
• Spark ML cookbook (Scala)
• Spark SQL cookbook (Scala)
• 不定期更新中

Submit the Application

$ spark-submit \
--master spark://192.168.10.100:7077 \
--packages "com.github.fommil.netlib:all:1.1.2,com.hankcs:hanlp:portable-1.3.4,mysql:mysql-connector-java:5.1.41" \
--class ws.vinta.albedo.UserProfileBuilder \
target/albedo-1.0.0-SNAPSHOT.jar

$ spark-submit \
--master spark://192.168.10.100:7077 \
--packages "com.github.fommil.netlib:all:1.1.2,com.hankcs:hanlp:portable-1.3.4,mysql:mysql-connector-java:5.1.41" \
--class ws.vinta.albedo.RepoProfileBuilder \
target/albedo-1.0.0-SNAPSHOT.jar

$ spark-submit \
--master spark://192.168.10.100:7077 \
--packages "com.github.fommil.netlib:all:1.1.2,com.hankcs:hanlp:portable-1.3.4,mysql:mysql-connector-java:5.1.41" \
--class ws.vinta.albedo.LogisticRegressionRanker \
target/albedo-1.0.0-SNAPSHOT.jar

ref:
https://vinta.ws/code/setup-spark-scala-and-maven-with-intellij-idea.html
https://spark.apache.org/docs/latest/submitting-applications.html
https://spoddutur.github.io/spark-notes/distribution_of_executors_cores_and_memory_for_spark_application

Load Data

我們之前已經利用 GitHub API 和 BigQuery 上的 GitHub Archive 收集了 150 萬筆的打星紀錄和所屬的用戶、repo 數據。目前有以下幾個數據集，大致上是照著 GitHub API 建立的，欄位分別如下：

rawUserInfoDS.printSchema()
// root
 // |-- user_id: integer (nullable = true)
 // |-- user_login: string (nullable = true)
 // |-- user_account_type: string (nullable = true)
 // |-- user_name: string (nullable = true)
 // |-- user_company: string (nullable = true)
 // |-- user_blog: string (nullable = true)
 // |-- user_location: string (nullable = true)
 // |-- user_email: string (nullable = true)
 // |-- user_bio: string (nullable = true)
 // |-- user_public_repos_count: integer (nullable = true)
 // |-- user_public_gists_count: integer (nullable = true)
 // |-- user_followers_count: integer (nullable = true)
 // |-- user_following_count: integer (nullable = true)
 // |-- user_created_at: timestamp (nullable = true)
 // |-- user_updated_at: timestamp (nullable = true)

rawRepoInfoDS.printSchema()
// |-- repo_id: integer (nullable = true)
 // |-- repo_owner_id: integer (nullable = true)
 // |-- repo_owner_username: string (nullable = true)
 // |-- repo_owner_type: string (nullable = true)
 // |-- repo_name: string (nullable = true)
 // |-- repo_full_name: string (nullable = true)
 // |-- repo_description: string (nullable = true)
 // |-- repo_language: string (nullable = true)
 // |-- repo_created_at: timestamp (nullable = true)
 // |-- repo_updated_at: timestamp (nullable = true)
 // |-- repo_pushed_at: timestamp (nullable = true)
 // |-- repo_homepage: string (nullable = true)
 // |-- repo_size: integer (nullable = true)
 // |-- repo_stargazers_count: integer (nullable = true)
 // |-- repo_forks_count: integer (nullable = true)
 // |-- repo_subscribers_count: integer (nullable = true)
 // |-- repo_is_fork: boolean (nullable = true)
 // |-- repo_has_issues: boolean (nullable = true)
 // |-- repo_has_projects: boolean (nullable = true)
 // |-- repo_has_downloads: boolean (nullable = true)
 // |-- repo_has_wiki: boolean (nullable = true)
 // |-- repo_has_pages: boolean (nullable = true)
 // |-- repo_open_issues_count: integer (nullable = true)
 // |-- repo_topics: string (nullable = true)

rawStarringDS.printSchema()
// root
 // |-- user_id: integer (nullable = true)
 // |-- repo_id: integer (nullable = true)
 // |-- starred_at: timestamp (nullable = true)
 // |-- starring: double (nullable = true)

ref:
https://www.githubarchive.org/
http://ghtorrent.org/

載入資料之後，要做的第一件事應該就是 Exploratory Data Analysis (EDA) 了，把玩一下手上的數據。建議大家可以試試 Apache Zeppelin 或是 Databricks 的 Notebook，除了內建 Spark 支援的所有語言之外，也整合了 NoSQL 和 JDBC 支援的資料庫，要畫圖表也很方便，簡直比 Jupyter Notebook 還好用了。

ref:
https://zeppelin.apache.org/
https://databricks.com/

Build User Profile / Item Profile

在這個專案中，最主要的數據主體就是 user 和 repo，所以我們針對兩者各自建立 User Profile 和 Item Profile，作為之後在模型訓練階段會使用的特徵。我們把這個步驟跟模型訓練的流程分開，這樣對整個架構的搭建會更有彈性。實務上，我們可以用 user id 或 item id 當 key，直接把製作好的特徵存進 Redis 或其他 schemaless 的 NoSQL 資料庫，方便之後給多個模型取用；在做 real-time 推薦時，也可以很快地拿到特徵，只需要重新計算部份欄位即可。

不過因為這裡主要用的是來自 GitHub API 的資料，某種程度上人家已經幫我們做了很多資料清理和正規化的動作了，但是在現實中，你的系統要處理的數據通常不會這麼乾淨，可能來自各種 data source、有著各種格式，還會隨著時間而改變，通常得花上不少力氣做 Extract, Transform, Load (ETL)，所以最好在寫 log（埋點）的時候就溝通好。而且在 production 環境中，數據是會一直變動的，要確保數據的時效性和容錯性，很重要的一個部分就是 monitoring。

ref:
http://www.algorithmdog.com/ad-rec-deploy
https://tech.meituan.com/online-feature-system02.html

礙於篇幅有限，以下的文章中我們只會挑幾個重要的部分說明。簡單說，在這個步驟的最後，我們會得到 userProfileDF 和 repoProfileDF 這兩個 DataFrame，分別存放製作好的特徵。詳細的程式碼如下：

ref:
https://github.com/vinta/albedo/blob/master/src/main/scala/ws/vinta/albedo/UserProfileBuilder.scala
https://github.com/vinta/albedo/blob/master/src/main/scala/ws/vinta/albedo/RepoProfileBuilder.scala

Feature Engineering

以推薦系統為例，特徵可以分成以下四種：

• 用戶特徵：用戶本身的各種屬性，例如 user id、性別、職業或所在的城市等
• 物品特徵：物品本身的各種屬性，例如 item id、作者、標題、分類、評分或所屬的標籤等
• 交互特徵：用戶對物品做出的某項行為，該行為的 aggregation 或交叉特徵，例如是否看過同類型的電影、最近聽的歌曲的曲風分佈或上週買了多少高單價的商品
• 上下文特徵：用戶對物品做出的某項行為，該行為的 metadata，例如發生的時間、使用的裝置或當前的 GPS 位置等

有些特徵是在資料採集階段就能拿到，有些特徵則會需要額外的步驟（例如透過外部的 API 或是其他模型）才能取得，也有些特徵必須即時更新。順道一提，因為我們要預測的是「某個用戶會不會打星某個 repo」，所以下述特徵裡的 user 可以是 repo stargazer 也可以是 repo owner。

原始特徵：

• 用戶特徵
  • user_id
  • user_login
  • user_name
  • user_email
  • user_blog
  • user_bio
  • user_company
  • user_location
  • user_followers_coung
  • user_following_count
  • user_public_repos_count
  • user_public_gists_count
  • user_created_at
  • user_updated_at
• 物品特徵
  • repo_id
  • repo_name
  • repo_owner
  • repo_owner_type
  • repo_language
  • repo_description
  • repo_homepage
  • repo_subscribers_count
  • repo_stargazers_count
  • repo_forks_count
  • repo_size
  • repo_created_at
  • repo_updated_at
  • repo_pushed_at
  • repo_has_issues
  • repo_has_projects
  • repo_has_downloads
  • repo_has_wiki
  • repo_has_pages
  • repo_open_issues_count
  • repo_topics
• 交互特徵
  • user_stars_repo
  • user_follows_user
• 上下文特徵
  • user_repo_starred_at

發想特徵：

• 用戶特徵
  • user_days_between_created_at_today: 該用戶的註冊日期距離今天過了幾年
  • user_days_between_updated_at_today: 該用戶的更新日期距離今天過了幾天
  • user_repos_avg_stargazers_count: 該用戶名下的所有 repo（不含 fork 的）的平均星星數
  • user_organizations: 該用戶屬於哪些組織
  • user_has_null: 該用戶至少有一個欄位是 null
  • user_has_blog: 該用戶有沒有網站
  • user_is_freelancer: 該用戶的 bio 中是否包含 Freelancer 等字眼
  • user_is_junior: 該用戶的 bio 中是否包含 Beginner 或 Junior 等字眼
  • user_is_lead: 該用戶的 bio 中是否包含 Team Lead、Architect、Creator、CTO 或 VP of Engineering 等字眼
  • user_is_scholar: 該用戶的 bio 中是否包含 Researcher、Scientist、PhD 或 Professor 等字眼
  • user_is_pm: 該用戶的 bio 中是否包含 Product Manager 等字眼
  • user_knows_backend: 該用戶的 bio 中是否包含 Backend 或 Back end 等字眼
  • user_knows_data: 該用戶的 bio 中是否包含 Machine Learning、Deep Learning 或 Data Science 等字眼
  • user_knows_devops: 該用戶的 bio 中是否包含 DevOps、SRE、SysAdmin 或 Infrastructure 等字眼
  • user_knows_frontend: 該用戶的 bio 中是否包含 Frontend 或 Front end 等字眼
  • user_knows_mobile: 該用戶的 bio 中是否包含 Mobile、iOS 或 Android 等字眼
  • user_knows_recsys: 該用戶的 bio 中是否包含 Recommender System、Data Mining 或 Information Retrieval 等字眼
  • user_knows_web: 該用戶的 bio 中是否包含 Web Development 或 Fullstack 等字眼
• 物品特徵
  • repo_created_at_days_since_today: 該 repo 的建立日期距離今天過了幾天
  • repo_updated_at_days_since_today: 該 repo 的更新日期距離今天過了幾天
  • repo_pushed_at_days_since_today: 該 repo 的提交日期距離今天過了幾天
  • repo_stargazers_count_in_30days: 該 repo 在 30 天內收到的星星數
  • repo_subscribers_stargazers_ratio: 該 repo 的 watch 數和 star 數的比例
  • repo_forks_stargazers_ratio: 該 repo 的 fork 數和 star 數的比例
  • repo_open_issues_stargazers_ratio: 該 repo 的 數和 star 數的比例
  • repo_releases_count: 該 repo 的 release 或 tag 數
  • repo_lisence: 該 repo 的授權條款
  • repo_readme: 該 repo 的 README 內容
  • repo_has_null: 該 repo 有至少一個欄位是 null
  • repo_has_readme: 該 repo 是否有 README 檔案
  • repo_has_changelog: 該 repo 是否有 CHANGELOG 檔案
  • repo_has_contributing: 該 repo 是否有 CONTRIBUTING 檔案
  • repo_has_tests: 該 repo 是否有測試
  • repo_has_ci: 該 repo 是否有 CI
  • repo_has_dockerfile: 該 repo 是否有 Dockerfile
  • repo_is_unmaintained: 該 repo 是否不再維護了
  • repo_is_awesome: 該 repo 是否被收錄進任何的 awesome-xxx 列表裡
  • repo_is_vinta_starred: 該 repo 是否被 @vinta aka 本文的作者打星了
• 交互特徵
  • user_starred_repos_count: 該用戶總共打星了多少 repo
  • user_avg_daily_starred_repos_count: 該用戶平均每天打星多少 repo
  • user_forked_repos_count: 該用戶總共 fork 了多少 repo
  • user_follower_following_count_ratio: 該用戶的 follower 數和 following 數的比例
  • user_recent_searched_keywords: 該用戶最近搜尋的 50 個關鍵字
  • user_recent_commented_repos: 該用戶最近留言的 50 個 repo
  • user_recent_watched_repos: 該用戶最近訂閱的 50 個 repo
  • user_recent_starred_repos_descriptions: 該用戶最近打星的 50 個 repo 的描述
  • user_recent_starred_repos_languages: 該用戶最近打星的 50 個 repo 的語言
  • user_recent_starred_repos_topics: 該用戶最近打星的 50 個 repo 的標籤
  • user_follows_repo_owner: 該用戶是否追蹤該 repo 的作者
  • repo_language_index_in_user_recent_repo_languages: 該 repo 的語言出現在該用戶最近打星的語言列表的順序
  • repo_language_count_in_user_recent_repo_languages: 該 repo 的語言出現在該用戶最近打星的語言列表的次數
  • repo_topics_user_recent_topics_similarity: 該 repo 的標籤與該用戶最近打星的標籤列表的相似度
• 上下文特徵
  • als_model_prediction: 來自 ALS 模型的預測值，該用戶對該 repo 的偏好程度
  • gbdt_model_index: 來自 GBDT 模型的 tree index，該 observation 的自動特徵

Feature Engineering 特徵工程中常見的方法
https://vinta.ws/code/feature-engineering.html

Detect Outliers

除了缺失值之外，離群值（異常值）也是需要注意的地方。如果是 continuous 特徵，用 box plot 可以很快地發現離群值；如果是 categorical 特徵，可以 SELECT COUNT(*) ... GROUP BY 一下，然後用 bar chart 查看每個 category 的數量。取決於你所要解決的問題，異常值可能可以直接忽略，也可能需要特別對待，例如搞清楚異常值出現的原因，是資料採集時的差錯或是某種隱含的深層的因素之類的。

ref:
https://www.analyticsvidhya.com/blog/2016/01/guide-data-exploration/
https://www.slideshare.net/tw_dsconf/123-70852901

Impute Missing Values

可以利用 df.describe().show() 查看各個欄位的統計數據：count、mean、stddev、min 和 max。除了使用 df.where("some_column IS NULL") 之外，比較不同欄位的 count 差異也可以很快地發現哪些欄位有缺失值。順便觀察一下有缺失值的欄位和 target variable 有沒有什麼關聯。

這裡直接對 null 和 NaN 數據填充缺失值，因為以下幾個欄位都是字串類型，所以直接改成空字串，方便後續的處理。然後順便做一個 has_null 的特徵。

針對 user：

import org.apache.spark.sql.functions._

val nullableColumnNames = Array("user_name", "user_company", "user_blog", "user_location", "user_bio")

val imputedUserInfoDF = rawUserInfoDS
  .withColumn("user_has_null", when(nullableColumnNames.map(rawUserInfoDS(_).isNull).reduce(_ || _), true).otherwise(false))
  .na.fill("", nullableColumnNames)

針對 repo：

import org.apache.spark.sql.functions._

val nullableColumnNames = Array("repo_description", "repo_homepage")

val imputedRepoInfoDF = rawRepoInfoDS
  .withColumn("repo_has_null", when(nullableColumnNames.map(rawRepoInfoDS(_).isNull).reduce(_ || _), true).otherwise(false))
  .na.fill("", nullableColumnNames)

ref:
https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.DataFrameNaFunctions

如果是數值類型的欄位可以考慮使用 Imputer。

ref:
https://spark.apache.org/docs/latest/ml-features.html#imputer

Clean Data

針對 user，用 User-defined Function 對幾個文字欄位做一些正規化的處理：

import org.apache.spark.sql.functions._

val cleanUserInfoDF = imputedUserInfoDF
  .withColumn("user_clean_company", cleanCompanyUDF($"user_company"))
  .withColumn("user_clean_location", cleanLocationUDF($"user_location"))
  .withColumn("user_clean_bio", lower($"user_bio"))

針對 repo，過濾掉一些 repo_stargazers_count 太多和太少、description 欄位含有 "unmaintained" 或 "assignment" 等字眼的項目：

val reducedRepoInfo = imputedRepoInfoDF
  .where($"repo_is_fork" === false)
  .where($"repo_forks_count" <= 90000)
  .where($"repo_stargazers_count".between(30, 100000))

val unmaintainedWords = Array("%unmaintained%", "%no longer maintained%", "%deprecated%", "%moved to%")
val assignmentWords = Array("%assignment%", "%作業%", "%作业%")
val demoWords = Array("test", "%demo project%")
val blogWords = Array("my blog")

val cleanRepoInfoDF = reducedRepoInfo
  .withColumn("repo_clean_description", lower($"repo_description"))
  .withColumn("repo_is_unmaintained", when(unmaintainedWords.map($"repo_clean_description".like(_)).reduce(_ or _), true).otherwise(false))
  .withColumn("repo_is_assignment", when(assignmentWords.map($"repo_clean_description".like(_)).reduce(_ or _), true).otherwise(false))
  .withColumn("repo_is_demo", when(demoWords.map($"repo_clean_description".like(_)).reduce(_ or _) and $"repo_stargazers_count" <= 40, true).otherwise(false))
  .withColumn("repo_is_blog", when(blogWords.map($"repo_clean_description".like(_)).reduce(_ or _) and $"repo_stargazers_count" <= 40, true).otherwise(false))
  .where($"repo_is_unmaintained" === false)
  .where($"repo_is_assignment" === false)
  .where($"repo_is_demo" === false)
  .where($"repo_is_blog" === false)
  .withColumn("repo_clean_language", lower($"repo_language"))
  .withColumn("repo_clean_topics", lower($"repo_topics"))

Construct Features

針對 user，根據上述的「發想特徵」，製作出新的特徵：

import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._

val webThings = Array("web", "fullstack", "full stack")
val backendThings = Array("backend", "back end", "back-end")
val frontendThings = Array("frontend", "front end", "front-end")
val mobileThings = Array("mobile", "ios", "android")
val devopsThings = Array("devops", "sre", "admin", "infrastructure")
val dataThings = Array("machine learning", "deep learning", "data scien", "data analy")
val recsysThings = Array("data mining", "recommend", "information retrieval")

val leadTitles = Array("team lead", "architect", "creator", "director", "cto", "vp of engineering")
val scholarTitles = Array("researcher", "scientist", "phd", "professor")
val freelancerTitles = Array("freelance")
val juniorTitles = Array("junior", "beginner", "newbie")
val pmTitles = Array("product manager")

val userStarredReposCountDF = rawStarringDS
  .groupBy($"user_id")
  .agg(count("*").alias("user_starred_repos_count"))

val starringRepoInfoDF = rawStarringDS
  .select($"user_id", $"repo_id", $"starred_at")
  .join(rawRepoInfoDS, Seq("repo_id"))

val userTopLanguagesDF = starringRepoInfoDF
  .withColumn("rank", rank.over(Window.partitionBy($"user_id").orderBy($"starred_at".desc)))
  .where($"rank" <= 50)
  .groupBy($"user_id")
  .agg(collect_list(lower($"repo_language")).alias("user_recent_repo_languages"))
  .select($"user_id", $"user_recent_repo_languages")

val userTopTopicsDF = starringRepoInfoDF
  .where($"repo_topics" =!= "")
  .withColumn("rank", rank.over(Window.partitionBy($"user_id").orderBy($"starred_at".desc)))
  .where($"rank" <= 50)
  .groupBy($"user_id")
  .agg(concat_ws(",", collect_list(lower($"repo_topics"))).alias("temp_user_recent_repo_topics"))
  .select($"user_id", split($"temp_user_recent_repo_topics", ",").alias("user_recent_repo_topics"))

val userTopDescriptionDF = starringRepoInfoDF
  .where($"repo_description" =!= "")
  .withColumn("rank", rank.over(Window.partitionBy($"user_id").orderBy($"starred_at".desc)))
  .where($"rank" <= 50)
  .groupBy($"user_id")
  .agg(concat_ws(" ", collect_list(lower($"repo_description"))).alias("user_recent_repo_descriptions"))
  .select($"user_id", $"user_recent_repo_descriptions")

val constructedUserInfoDF = cleanUserInfoDF
  .withColumn("user_knows_web", when(webThings.map($"user_clean_bio".like(_)).reduce(_ or _), true).otherwise(false))
  .withColumn("user_knows_backend", when(backendThings.map($"user_clean_bio".like(_)).reduce(_ or _), true).otherwise(false))
  .withColumn("user_knows_frontend", when(frontendThings.map($"user_clean_bio".like(_)).reduce(_ or _), true).otherwise(false))
  .withColumn("user_knows_mobile", when(mobileThings.map($"user_clean_bio".like(_)).reduce(_ or _), true).otherwise(false))
  .withColumn("user_knows_devops", when(devopsThings.map($"user_clean_bio".like(_)).reduce(_ or _), true).otherwise(false))
  .withColumn("user_knows_data", when(dataThings.map($"user_clean_bio".like(_)).reduce(_ or _), true).otherwise(false))
  .withColumn("user_knows_recsys", when(recsysThings.map($"user_clean_bio".like(_)).reduce(_ or _), true).otherwise(false))
  .withColumn("user_is_lead", when(leadTitles.map($"user_clean_bio".like(_)).reduce(_ or _), true).otherwise(false))
  .withColumn("user_is_scholar", when(scholarTitles.map($"user_clean_bio".like(_)).reduce(_ or _), true).otherwise(false))
  .withColumn("user_is_freelancer", when(freelancerTitles.map($"user_clean_bio".like(_)).reduce(_ or _), true).otherwise(false))
  .withColumn("user_is_junior", when(juniorTitles.map($"user_clean_bio".like(_)).reduce(_ or _), true).otherwise(false))
  .withColumn("user_is_pm", when(pmTitles.map($"user_clean_bio".like(_)).reduce(_ or _), true).otherwise(false))
  .withColumn("user_followers_following_ratio", round($"user_followers_count" / ($"user_following_count" + lit(1.0)), 3))
  .withColumn("user_days_between_created_at_today", datediff(current_date(), $"user_created_at"))
  .withColumn("user_days_between_updated_at_today", datediff(current_date(), $"user_updated_at"))
  .join(userStarredReposCountDF, Seq("user_id"))
  .withColumn("user_avg_daily_starred_repos_count", round($"user_starred_repos_count" / ($"user_days_between_created_at_today" + lit(1.0)), 3))
  .join(userTopDescriptionDF, Seq("user_id"))
  .join(userTopTopicsDF, Seq("user_id"))
  .join(userTopLanguagesDF, Seq("user_id"))

針對 repo，根據上述的「發想特徵」，製作出新的特徵，意思到了就好：

import org.apache.spark.sql.functions._

val vintaStarredRepos = rawStarringDS
  .where($"user_id" === 652070)
  .select($"repo_id".as[Int])
  .collect()
  .to[List]

val constructedRepoInfoDF = cleanRepoInfoDF
  .withColumn("repo_has_activities_in_60days", datediff(current_date(), $"repo_pushed_at") <= 60)
  .withColumn("repo_has_homepage", when($"repo_homepage" === "", false).otherwise(true))
  .withColumn("repo_is_vinta_starred", when($"repo_id".isin(vintaStarredRepos: _*), true).otherwise(false))
  .withColumn("repo_days_between_created_at_today", datediff(current_date(), $"repo_created_at"))
  .withColumn("repo_days_between_updated_at_today", datediff(current_date(), $"repo_updated_at"))
  .withColumn("repo_days_between_pushed_at_today", datediff(current_date(), $"repo_pushed_at"))
  .withColumn("repo_subscribers_stargazers_ratio", round($"repo_subscribers_count" / ($"repo_stargazers_count" + lit(1.0)), 3))
  .withColumn("repo_forks_stargazers_ratio", round($"repo_forks_count" / ($"repo_stargazers_count" + lit(1.0)), 3))
  .withColumn("repo_open_issues_stargazers_ratio", round($"repo_open_issues_count" / ($"repo_stargazers_count" + lit(1.0)), 3))
  .withColumn("repo_text", lower(concat_ws(" ", $"repo_owner_username", $"repo_name", $"repo_language", $"repo_description")))

ref:
https://databricks.com/blog/2015/09/16/apache-spark-1-5-dataframe-api-highlights.html

Convert Features

針對 user，這裡主要是對一些 categorical 特徵作 binning：

import org.apache.spark.sql.functions._

val companyCountDF = cleanUserInfoDF
  .groupBy($"user_clean_company")
  .agg(count("*").alias("count_per_user_company"))

val locationCountDF = cleanUserInfoDF
  .groupBy($"user_clean_location")
  .agg(count("*").alias("count_per_user_location"))

val transformedUserInfoDF = constructedUserInfoDF
  .join(companyCountDF, Seq("user_clean_company"))
  .join(locationCountDF, Seq("user_clean_location"))
  .withColumn("user_has_blog", when($"user_blog" === "", 0.0).otherwise(1.0))
  .withColumn("user_binned_company", when($"count_per_user_company" <= 5, "__other").otherwise($"user_clean_company"))
  .withColumn("user_binned_location", when($"count_per_user_location" <= 50, "__other").otherwise($"user_clean_location"))

針對 repo：

import org.apache.spark.sql.functions._

val languagesDF = cleanRepoInfoDF
  .groupBy($"repo_clean_language")
  .agg(count("*").alias("count_per_repo_language"))
  .select($"repo_clean_language", $"count_per_repo_language")
  .cache()

val transformedRepoInfoDF = constructedRepoInfoDF
  .join(languagesDF, Seq("repo_clean_language"))
  .withColumn("repo_binned_language", when($"count_per_repo_language" <= 30, "__other").otherwise($"repo_clean_language"))
  .withColumn("repo_clean_topics", split($"repo_topics", ","))

ref:
https://docs.databricks.com/spark/latest/mllib/binary-classification-mllib-pipelines.html

Prepare the Feature Pipeline

我們過濾掉那些打星了超多 repo 的用戶。從收集到的數據發現，有些用戶甚至打星了一兩萬個 repo，這些用戶可能是個爬蟲專用帳號或是他看到什麼就打星什麼，推薦系統對這樣的用戶來說可能沒什麼意義，還不如從數據集中拿掉。

import org.apache.spark.sql.functions._

val maxStarredReposCount = 2000

val userStarredReposCountDF = rawStarringDS
  .groupBy($"user_id")
  .agg(count("*").alias("user_starred_repos_count"))

val reducedStarringDF = rawStarringDS
  .join(userStarredReposCountDF, Seq("user_id"))
  .where($"user_starred_repos_count" <= maxStarredReposCount)
  .select($"user_id", $"repo_id", $"starred_at", $"starring")

val profileStarringDF = reducedStarringDF
  .join(userProfileDF, Seq("user_id"))
  .join(repoProfileDF, Seq("repo_id"))

Build the Feature Pipeline

把處理特徵的一連串流程寫成 Spark ML Pipeline，方便抽換或是加入新的 Transformer，例如 Standardization、One-hot Encoding 和 Word2Vec，也把 ALS 模型的預測值做為其中一項特徵。

import org.apache.spark.ml.feature._
import org.apache.spark.ml.recommendation.ALSModel
import ws.vinta.albedo.transformers.UserRepoTransformer

val profileStarringDF = reducedStarringDF
  .join(userProfileDF, Seq("user_id"))
  .join(repoProfileDF, Seq("repo_id"))
  .cache()

categoricalColumnNames += "user_id"
categoricalColumnNames += "repo_id"

val userRepoTransformer = new UserRepoTransformer()
  .setInputCols(Array("repo_language", "user_recent_repo_languages"))

continuousColumnNames += "repo_language_index_in_user_recent_repo_languages"
continuousColumnNames += "repo_language_count_in_user_recent_repo_languages"

val alsModelPath = s"${settings.dataDir}/${settings.today}/alsModel.parquet"
val alsModel = ALSModel.load(alsModelPath)
  .setUserCol("user_id")
  .setItemCol("repo_id")
  .setPredictionCol("als_score")
  .setColdStartStrategy("drop")

continuousColumnNames += "als_score"

val categoricalTransformers = categoricalColumnNames.flatMap((columnName: String) => {
  val stringIndexer = new StringIndexer()
    .setInputCol(columnName)
    .setOutputCol(s"${columnName}__idx")
    .setHandleInvalid("keep")

  val oneHotEncoder = new OneHotEncoder()
    .setInputCol(s"${columnName}__idx")
    .setOutputCol(s"${columnName}__ohe")
    .setDropLast(false)

  Array(stringIndexer, oneHotEncoder)
})

val listTransformers = listColumnNames.flatMap((columnName: String) => {
  val countVectorizerModel = new CountVectorizer()
    .setInputCol(columnName)
    .setOutputCol(s"${columnName}__cv")
    .setMinDF(10)
    .setMinTF(1)

  Array(countVectorizerModel)
})

val textTransformers = textColumnNames.flatMap((columnName: String) => {
  val hanLPTokenizer = new HanLPTokenizer()
    .setInputCol(columnName)
    .setOutputCol(s"${columnName}__words")
    .setShouldRemoveStopWords(true)

  val stopWordsRemover = new StopWordsRemover()
    .setInputCol(s"${columnName}__words")
    .setOutputCol(s"${columnName}__filtered_words")
    .setStopWords(StopWordsRemover.loadDefaultStopWords("english"))
  val word2VecModelPath = s"${settings.dataDir}/${settings.today}/word2VecModel.parquet"
  val word2VecModel = Word2VecModel.load(word2VecModelPath)
    .setInputCol(s"${columnName}__filtered_words")
    .setOutputCol(s"${columnName}__w2v")

  Array(hanLPTokenizer, stopWordsRemover, word2VecModel)
})

val finalBooleanColumnNames = booleanColumnNames.toArray
val finalContinuousColumnNames = continuousColumnNames.toArray
val finalCategoricalColumnNames = categoricalColumnNames.map(columnName => s"${columnName}__ohe").toArray
val finalListColumnNames = listColumnNames.map(columnName => s"${columnName}__cv").toArray
val finalTextColumnNames = textColumnNames.map(columnName => s"${columnName}__w2v").toArray
val vectorAssembler = new SimpleVectorAssembler()
  .setInputCols(finalBooleanColumnNames ++ finalContinuousColumnNames ++ finalCategoricalColumnNames ++ finalListColumnNames ++ finalTextColumnNames)
  .setOutputCol("features")

val featureStages = mutable.ArrayBuffer.empty[PipelineStage]
featureStages += userRepoTransformer
featureStages += alsModel
featureStages ++= categoricalTransformers
featureStages ++= listTransformers
featureStages ++= textTransformers
featureStages += vectorAssembler

val featurePipeline = new Pipeline().setStages(featureStages.toArray)
val featurePipelineModel = featurePipeline.fit(profileStarringDF)

ref:
https://spark.apache.org/docs/latest/ml-pipeline.html
https://spark.apache.org/docs/latest/ml-features.html

Handle Imbalanced Data

因為我們要訓練一個 Binary Classification 二元分類模型，會同時需要 positive（正樣本）和 negative（負樣本）。但是我們的原始數據 rawStarringDS 都是正樣本，也就是說我們只有「用戶有對哪些 repo 打星的資料」（正樣本），卻沒有「用戶沒有對哪些 repo 打星的資料」（負樣本）。我們當然是可以用「所有用戶沒有打星的 repo 做為負樣本」，但是考慮到這種做法產生的負樣本的數量實在太大，而且也不太合理，因為那些用戶沒有打星的 repo 不見得是因為他不喜歡，可能只是因為他不知道有那個 repo 存在。

我們後來採用的做法是「用熱門但是用戶沒有打星的 repo 做為負樣本」，我們寫了一個 Spark Transformer 來做這件事：

import ws.vinta.albedo.transformers.NegativeBalancer

import scala.collection.mutable

val sc = spark.sparkContext

val popularReposDS = loadPopularRepoDF()
val popularRepos = popularReposDS
  .select($"repo_id".as[Int])
  .collect()
  .to[mutable.LinkedHashSet]
val bcPopularRepos = sc.broadcast(popularRepos)

val negativeBalancer = new NegativeBalancer(bcPopularRepos)
  .setUserCol("user_id")
  .setItemCol("repo_id")
  .setTimeCol("starred_at")
  .setLabelCol("starring")
  .setNegativeValue(0.0)
  .setNegativePositiveRatio(2.0)
val balancedStarringDF = negativeBalancer.transform(reducedStarringDF)

ref:
https://github.com/vinta/albedo/blob/master/src/main/scala/ws/vinta/albedo/evaluators/RankingEvaluator.scala
http://www.kdnuggets.com/2017/06/7-techniques-handle-imbalanced-data.html

Split Data

直接使用 holdout 的方式，隨機分配不同的 row 到 training set 和 test set。其他的做法可能是根據時間來拆分，用以前的數據來預測之後的行為。

val profileBalancedStarringDF = balancedStarringDF
  .join(userProfileDF, Seq("user_id"))
  .join(repoProfileDF, Seq("repo_id"))

val tmpDF = featurePipelineModel.transform(profileBalancedStarringDF)
val keepColumnName = tmpDF.columns.filter((columnName: String) => {
  !columnName.endsWith("__idx") &&
  !columnName.endsWith("__ohe") &&
  !columnName.endsWith("__cv") &&
  !columnName.endsWith("__words") &&
  !columnName.endsWith("__filtered_words") &&
  !columnName.endsWith("__w2v")
})
val featuredBalancedStarringDF = tmpDF.select(keepColumnName.map(col): _*)

val Array(trainingFeaturedDF, testFeaturedDF) = featuredBalancedStarringDF.randomSplit(Array(0.9, 0.1))

Build the Model Pipeline

為了方便之後的擴充性，這裡也使用 Spark ML Pipeline 的寫法。Spark ML 的 LogisticRegression 可以額外設置一個 weightCol 來調整不同 row 的權重。

import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.{Pipeline, PipelineStage}

import scala.collection.mutable

val weightSQL = """
SELECT *,
       1.0 AS default_weight,
       IF (starring = 1.0, 0.9, 0.1) AS positive_weight,
       IF (starring = 1.0 AND datediff(current_date(), starred_at) <= 365, 0.9, 0.1) AS recent_starred_weight
FROM __THIS__
""".stripMargin
val weightTransformer = new SQLTransformer()
  .setStatement(weightSQL)

val lr = new LogisticRegression()
  .setMaxIter(200)
  .setRegParam(0.7)
  .setElasticNetParam(0.0)
  .setStandardization(true)
  .setLabelCol("starring")
  .setFeaturesCol("standard_features")
  .setWeightCol("recent_starred_weight")

val modelStages = mutable.ArrayBuffer.empty[PipelineStage]
modelStages += weightTransformer
modelStages += lr

val modelPipeline = new Pipeline().setStages(modelStages.toArray)
val modelPipelineModel = modelPipeline.fit(trainingFeaturedDF)

ref:
https://spark.apache.org/docs/latest/ml-classification-regression.html

Evaluate the Model: Classification

因為 Logistic Regression 是二元分類模型，所以我們可以用 Spark ML 的 BinaryClassificationEvaluator 來評估結果。不過因為我們做的是推薦系統，真正在乎的是 Top N 的排序問題，所以這裡的 AUC 的數值參考一下就好。

import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator

val testRankedDF = modelPipelineModel.transform(testFeaturedDF)

val binaryClassificationEvaluator = new BinaryClassificationEvaluator()
  .setMetricName("areaUnderROC")
  .setRawPredictionCol("rawPrediction")
  .setLabelCol("starring")

val classificationMetric = binaryClassificationEvaluator.evaluate(testRankedDF)
println(s"${binaryClassificationEvaluator.getMetricName} = $classificationMetric")
// areaUnderROC = 0.9450631491281277

ref:
https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
https://docs.databricks.com/spark/latest/mllib/binary-classification-mllib-pipelines.html

Generate Candidates

推薦系統的另外一個重要部分就是產生候選物品集，這裡我們使用以下幾種方式：

• ALS: 協同過濾的推薦
• Content-based: 基於內容的推薦
• Popularity: 基於熱門的推薦

不過因為這篇文章的主題是排序和特徵工程的 Machine Learning Pipeline，所以產生候選物品集的部分就不多說了，有興趣的人可以直接看底下連結的 source code 或是這個系列的其他文章。

import ws.vinta.albedo.recommenders.ALSRecommender
import ws.vinta.albedo.recommenders.ContentRecommender
import ws.vinta.albedo.recommenders.PopularityRecommender

val topK = 30

val alsRecommender = new ALSRecommender()
  .setUserCol("user_id")
  .setItemCol("repo_id")
  .setTopK(topK)

val contentRecommender = new ContentRecommender()
  .setUserCol("user_id")
  .setItemCol("repo_id")
  .setTopK(topK)
  .setEnableEvaluationMode(true)

val popularityRecommender = new PopularityRecommender()
  .setUserCol("user_id")
  .setItemCol("repo_id")
  .setTopK(topK)

val recommenders = mutable.ArrayBuffer.empty[Recommender]
recommenders += alsRecommender
recommenders += contentRecommender
recommenders += popularityRecommender

val candidateDF = recommenders
  .map((recommender: Recommender) => recommender.recommendForUsers(testUserDF))
  .reduce(_ union _)
  .select($"user_id", $"repo_id")
  .distinct()

// 每個 Recommender 的結果類似這樣：
// +-------+-------+----------+------+
// |user_id|repo_id|score     |source|
// +-------+-------+----------+------+
// |652070 |1239728|0.6731846 |als   |
// |652070 |854078 |0.7187486 |als   |
// |652070 |1502338|0.70165294|als   |
// |652070 |1184678|0.7434903 |als   |
// |652070 |547708 |0.7956538 |als   |
// +-------+-------+----------+------+

ref:
https://github.com/vinta/albedo/blob/master/src/main/scala/ws/vinta/albedo/recommenders/ALSRecommender.scala
https://github.com/vinta/albedo/blob/master/src/main/scala/ws/vinta/albedo/recommenders/ContentRecommender.scala
https://github.com/vinta/albedo/blob/master/src/main/scala/ws/vinta/albedo/recommenders/PopularityRecommender.scala

Predict the Ranking

把這些候選物品集丟給我們訓練好的 Logistic Regression 模型來排序。結果中的 probability 欄位的第 0 項表示結果為 0 的機率（negative）、第 1 項表示結果為 1 的機率（positive）。

val profileCandidateDF = candidateDF
  .join(userProfileDF, Seq("user_id"))
  .join(repoProfileDF, Seq("repo_id"))

val featuredCandidateDF = featurePipelineModel
  .transform(profileCandidateDF)

val rankedCandidateDF = modelPipelineModel
  .transform(featuredCandidateDF)

// rankedCandidateDF 的結果類似這樣：
// +-------+--------+----------+----------------------------------------+
// |user_id|repo_id |prediction|probability                             |
// +-------+--------+----------+----------------------------------------+
// |652070 |83467664|1.0       |[0.12711894229094317,0.8728810577090568]|
// |652070 |55099616|1.0       |[0.1422859437320775,0.8577140562679224] |
// |652070 |42266235|1.0       |[0.1462014853157966,0.8537985146842034] |
// |652070 |78012800|1.0       |[0.15576081067098502,0.844239189329015] |
// |652070 |5928761 |1.0       |[0.16149848941925066,0.8385015105807493]|
// +-------+--------+----------+----------------------------------------+

ref:
https://stackoverflow.com/questions/37903288/what-do-colum-rawprediction-and-probability-of-dataframe-mean-in-spark-mllib

Evaluate the Model: Ranking

最後我們使用 Information Retrieval 領域中用來評價排序能力的指標 NDCG (Normalized Discounted Cumulative Gain) 來評估排序的結果。Spark MLlib 有現成的 RankingMetrics 可以用，但是它只適用於 RDD-based 的 API，所以我們改寫成適合 DataFrame-based 的 Evaluator。

import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
import ws.vinta.albedo.evaluators.RankingEvaluator

val userActualItemsDF = reducedStarringDF
  .withColumn("rank", rank().over(Window.partitionBy($"user_id").orderBy($"starred_at".desc)))
  .where($"rank" <= topK)
  .groupBy($"user_id")
  .agg(collect_list($"repo_id").alias("items"))

val userPredictedItemsDF = rankedCandidateDF
  .withColumn("rank", rank().over(Window.partitionBy($"user_id").orderBy(toArrayUDF($"probability").getItem(1).desc)))
  .where($"rank" <= topK)
  .groupBy($"user_id")
  .agg(collect_list($"repo_id").alias("items"))

val rankingEvaluator = new RankingEvaluator(userActualItemsDF)
  .setMetricName("NDCG@k")
  .setK(topK)
  .setUserCol("user_id")
  .setItemsCol("items")
val rankingMetric = rankingEvaluator.evaluate(userPredictedItemsDF)
println(s"${rankingEvaluator.getFormattedMetricName} = $rankingMetric")
// NDCG@30 = 0.021114356461615493

ref:
https://github.com/vinta/albedo/blob/master/src/main/scala/ws/vinta/albedo/evaluators/RankingEvaluator.scala
https://spark.apache.org/docs/latest/mllib-evaluation-metrics.html#ranking-systems
https://weekly.codetengu.com/issues/83#kOxuVxW

Scala is the first class citizen language for interacting with Apache Spark, but it's difficult to learn. This article is mostly about Spark ML - the new Spark Machine Learning library which was rewritten in DataFrame-based API.

Convert a String Categorical Feature into Numeric One

StringIndexer converts labels (categorical values) into numbers (0.0, 1.0, 2.0 and so on) which ordered by label frequencies, the most frequnet label gets 0. This method is able to handle unseen labels with optional strategies.

StringIndexer's inputCol accepts string, numeric and boolean types.

val df1 = spark.createDataFrame(Seq(
    (1, "Python"),
    (2, "C++"),
    (3, "C++"),
    (4, "JavaScript"),
    (5, "Python"),
    (6, "Python"),
    (7, "Go")
BinaryClassificationEvaluator
)).toDF("repo_id", "repo_language")

val df2 = spark.createDataFrame(Seq(
    (1, "Python"),
    (2, "C++"),
    (3, "C++"),
    (4, "JavaScript"),
    (5, "Python"),
    (6, "Python"),
    (7, "Go"),
    (8, "JavaScript"),
    (9, "Brainfuck"),
    (10, "Brainfuck"),
    (11, "Red")
)).toDF("repo_id", "repo_language")

import org.apache.spark.ml.feature.StringIndexer

val stringIndexer = new StringIndexer()
  .setInputCol("repo_language")
  .setOutputCol("repo_language_index")
  .setHandleInvalid("keep")
val stringIndexerModel = stringIndexer.fit(df1)

stringIndexerModel.labels
// Array[String] = Array(Python, C++, JavaScript, Go)

val indexedDF = stringIndexerModel.transform(df2)
indexedDF.show()
// +-------+-------------+-------------------+
// |repo_id|repo_language|repo_language_index|
// +-------+-------------+-------------------+
// |      1|       Python|                0.0|
// |      2|          C++|                1.0|
// |      3|          C++|                1.0|
// |      4|   JavaScript|                3.0|
// |      5|       Python|                0.0|
// |      6|       Python|                0.0|
// |      7|           Go|                2.0|
// |      8|   JavaScript|                3.0|
// |      9|    Brainfuck|                4.0| <- previously unseen
// |     10|    Brainfuck|                4.0| <- previously unseen
// |     11|          Red|                4.0| <- previously unseen
// +-------+-------------+-------------------+

ref:
https://spark.apache.org/docs/latest/ml-features.html#stringindexer
https://stackoverflow.com/questions/34681534/spark-ml-stringindexer-handling-unseen-labels
https://stackoverflow.com/questions/32277576/how-to-handle-categorical-features-with-spark-ml/32278617

Convert an Indexed Numeric Feature Back to the Original Categorical One

import org.apache.spark.ml.feature.IndexToString

val indexToString = new IndexToString()
  .setInputCol("repo_language_index")
  .setOutputCol("repo_language_ori")

val oriIndexedDF = indexToString.transform(indexedDF)
oriIndexedDF.show()
// +-------+-------------+-------------------+----------------------+
// |repo_id|repo_language|repo_language_index|     repo_language_ori|
// +-------+-------------+-------------------+----------------------+
// |      1|       Python|                0.0|                Python|
// |      2|          C++|                1.0|                   C++|
// |      3|          C++|                1.0|                   C++|
// |      4|   JavaScript|                2.0|            JavaScript|
// |      5|       Python|                0.0|                Python|
// |      6|       Python|                0.0|                Python|
// |      7|           Go|                3.0|                    Go|
// |      8|   JavaScript|                2.0|            JavaScript|
// |      9|    Brainfuck|                4.0|             __unknown| <- previously unseen
// |     10|    Brainfuck|                4.0|             __unknown| <- previously unseen
// |     11|          Red|                4.0|             __unknown| <- previously unseen
// +-------+-------------+-------------------+----------------------+

ref:
https://spark.apache.org/docs/latest/ml-features.html#indextostring

One-hot Encoding for Categorical Features

OneHotEncoder's input column only accepts numeric types. If you have string columns, you need to use StringIndexer to transform them into doubles, bessides, StringIndexer is able to properly deal with unseen values. In my humble opinion, you should always apply StringIndexer before OneHotEncoder.

Be careful that OneHotEncoder's vector length will be the maximun value in the column, you must apply OneHotEncoder on the union dataset of both training set and test set. Since OneHotEncoder does not accept empty string for name, you need to replace all empty strings with a placeholder, something like __empty.

import org.apache.spark.ml.feature.OneHotEncoder

val knownDF = spark.createDataFrame(Seq(
  (2, "b"),
  (3, "c"),
  (0, "x"),
  (6, "c"),
  (4, "a"),
  (1, "a"),
  (5, "a")
)).toDF("category_1", "category_2")

val unseenDF = spark.createDataFrame(Seq(
  (123, "e"),
  (6, "c"),
  (2, "b"),
  (456, "c"),
  (1, "a")
)).toDF("category_1", "category_2")

val knownOneHotDF = new OneHotEncoder()
  .setDropLast(true)
  .setInputCol("category_1")
  .setOutputCol("category_1_one_hot")
  .transform(knownDF)
knownOneHotDF.show()
// +----------+----------+------------------+
// |category_1|category_2|category_1_one_hot|
// +----------+----------+------------------+
// |         2|         b|     (6,[2],[1.0])|
// |         3|         c|     (6,[3],[1.0])|
// |         0|         x|     (6,[0],[1.0])|
// |         6|         c|         (6,[],[])|
// |         4|         a|     (6,[4],[1.0])|
// |         1|         a|     (6,[1],[1.0])|
// |         5|         a|     (6,[5],[1.0])|
// +----------+----------+------------------+

val unseenOneHotDF = new OneHotEncoder()
  .setDropLast(true)
  .setInputCol("category_1")
  .setOutputCol("category_1_one_hot")
  .transform(unseenDF)
unseenOneHotDF.show()
// +----------+----------+------------------+
// |category_1|category_2|category_1_one_hot|
// +----------+----------+------------------+
// |       123|         e| (456,[123],[1.0])|
// |         6|         c|   (456,[6],[1.0])|
// |         2|         b|   (456,[2],[1.0])|
// |       456|         c|       (456,[],[])|
// |         1|         a|   (456,[1],[1.0])|
// +----------+----------+------------------+

ref:
https://spark.apache.org/docs/latest/ml-features.html#onehotencoder
https://stackoverflow.com/questions/32277576/how-to-handle-categorical-features-with-spark-ml/40615508
https://stackoverflow.com/questions/33089781/spark-dataframe-handing-empty-string-in-onehotencoder

Create a Regular Expression Tokenizer

setGaps(true) 時的 pattern 是 match 分隔符；setGaps(false) 時的 pattern 則是 match 字。

import org.apache.spark.ml.feature.RegexTokenizer
import org.apache.spark.sql.functions._

val sentenceDF = spark.createDataFrame(Seq(
  (1, "Hi, I heard about Spark"),
  (2, "I wish Java could use case classes."),
  (3, "Deep,Learning,models,are,state-of-the-art"),
  (4, "fuck_yeah!!! No.")
)).toDF("id", "sentence")

val countTokensUDF = udf((words: Seq[String]) => words.length)

val regexTokenizer = new RegexTokenizer()
  .setInputCol("sentence")
  .setOutputCol("words")
  .setPattern("""[\w\-_]+""").setGaps(false)
  // .setPattern("""\W""").setGaps(true)
  // .setPattern("""[,. ]""").setGaps(true)
val tokenizedDF = regexTokenizer.transform(sentenceDF)

val df = tokenizedDF
  .select("sentence", "words")
  .withColumn("count", countTokensUDF($"words"))
// +-----------------------------------------+-----------------------------------------------+-----+
// |sentence                                 |words                                          |count|
// +-----------------------------------------+-----------------------------------------------+-----+
// |Hi, I heard about Spark                  |[hi, i, heard, about, spark]                   |5    |
// |I wish Java could use case classes.      |[i, wish, java, could, use, case, classes]     |7    |
// |Deep,Learning,models,are,state-of-the-art|[deep, learning, models, are, state-of-the-art]|5    |
// |fuck_yeah!!! No.                         |[fuck_yeah, no]                                |2    |
// +-----------------------------------------+-----------------------------------------------+-----+

ref:
https://spark.apache.org/docs/latest/ml-features.html#tokenizer

Handle Comma-seperated Categorical Column

You could use RegexTokenizer, CountVectorizer or HashingTF.

import org.apache.spark.ml.feature.{RegexTokenizer, CountVectorizer}

val df = spark.createDataFrame(Seq(
  (1, "Action,Sci-Fi"),
  (2, "Sci-Fi,Romance,Horror"),
  (3, "War,Horror")
)).toDF("movie_id", "genres")

val regexTokenizer = new RegexTokenizer()
  .setInputCol("genres")
  .setOutputCol("genres_words")
  .setPattern("""[\w\-_]+""").setGaps(false)
val wordsDF = regexTokenizer.transform(df)

val countVectorizerModel = new CountVectorizer()
  .setInputCol("genres_words")
  .setOutputCol("genres_vector")
  .setMinDF(1) // for whole corpus, delete any term that appears less then n times
  .setMinTF(1) // for each document, delete any term that appears less then n times
  .fit(wordsDF)
val countVectorDF = countModel.transform(wordsDF)

// HashingTF might suffer from potential hash collisions
// it's good to use a power of two
val hashingTF = new HashingTF()
  .setInputCol("genres_words")
  .setOutputCol("genres_htf_vector")
  .setNumFeatures(4)
val htfVectorDF = hashingTF.transform(countVectorDF)

htfVectorDF.show(false)
// +--------+---------------------+-------------------------+-------------------------+-------------------+
// |movie_id|genres               |genres_words             |genres_count_vector      |genres_htf_vector  |
// +--------+---------------------+-------------------------+-------------------------+-------------------+
// |1       |Action,Sci-Fi        |[action, sci-fi]         |(5,[0,3],[1.0,1.0])      |(4,[0],[2.0])      |
// |2       |Sci-Fi,Romance,Horror|[sci-fi, romance, horror]|(5,[0,1,4],[1.0,1.0,1.0])|(4,[0,2],[2.0,1.0])|
// |3       |War,Horror           |[war, horror]            |(5,[1,2],[1.0,1.0])      |(4,[0,2],[1.0,1.0])|
// +--------+---------------------+-------------------------+-------------------------+-------------------+

countModel.vocabulary
// Array(sci-fi, horror, action, romance, war)

ref:
https://spark.apache.org/docs/latest/ml-features.html#countvectorizer
https://spark.apache.org/docs/latest/ml-features.html#tf-idf

Train a Word2Vec Model

The output vector of any Word2Vec model is dense!

import org.apache.spark.ml.feature.Word2Vec

val df = spark.createDataFrame(Seq(
  (1, "Hi I heard about Apache Spark".toLowerCase().split(" ")),
  (2, "I wish Java could use case classes".toLowerCase().split(" ")),
  (3, "Logistic regression models are neat".toLowerCase().split(" ")),
  (4, "Apache Spark with Scala is awesome".toLowerCase().split(" ")),
  (5, Array("中文", "嘛ㄟ通", "but", "必須", "另外", "分詞"))
)).toDF("id", "words")

val word2Vec = new Word2Vec()
  .setInputCol("words")
  .setOutputCol("words_w2v")
  .setMaxIter(10)
  .setVectorSize(3)
  .setWindowSize(5)
  .setMinCount(1)
val word2VecModel = word2Vec.fit(df)

word2VecModel.transform(df)
// +---+------------------------------------------+----------------------------------------------------------+
// |id |words                                     |words_w2v                                                 |
// +---+------------------------------------------+----------------------------------------------------------+
// |1  |[hi, i, heard, about, apache, spark]      |[-0.02013699459393,-0.02995631482274,0.047685102870066956]|
// |2  |[i, wish, java, could, use, case, classes]|[-0.05012317272186,0.01141336891094,-0.03742781743806387] |
// |3  |[logistic, regression, models, are, neat] |[-0.04678827972413,0.032994424477,0.0010566591750830413]  |
// |4  |[apache, spark, with, scala, is, awesome] |[0.0265524153169,0.02056275321716,0.013326843579610188]   |
// |5  |[中文, 嘛ㄟ通, but, 必須, 另外, 分詞]         |[0.0571783996973,-0.02301329133545,0.013507421438892681]  |
// +---+------------------------------------------+----------------------------------------------------------+

val df2 = spark.createDataFrame(Seq(
  (6, Array("not-in-vocabularies", "neither", "no")),
  (7, Array("spark", "not-in-vocabularies")),
  (8, Array("not-in-vocabularies", "spark")),
  (9, Array("no", "not-in-vocabularies", "spark")),
  (10, Array("中文", "spark"))
)).toDF("id", "words")

word2VecModel.transform(df2)
// the order of words doesn't mater
// +---+-------------------------------------+-----------------------------------------------------------------+
// |id |words                                |words_w2v                                                        |
// +---+-------------------------------------+-----------------------------------------------------------------+
// |6  |[not-in-vocabularies, neither, no]   |[0.0,0.0,0.0]                                                    |
// |7  |[spark, hell_no, not-in-vocabularies]|[0.0027440187210838,-0.0529780387878418,0.05730373660723368]     |
// |8  |[hell_no, not-in-vocabularies, spark]|[0.0027440187210838,-0.0529780387878418,0.05730373660723368]     |
// |9  |[not-in-vocabularies, hell_no, spark]|[0.0027440187210838,-0.0529780387878418,0.05730373660723368]     |
// |10 |[no, not-in-vocabularies, spark]     |[0.0027440187210838,-0.0529780387878418,0.05730373660723368]     |
// |11 |[中文, spark]                         |[-0.009499748703092337,-0.018227852880954742,0.13357853144407272]|
// +---+-------------------------------------+-----------------------------------------------------------------+

anotherWord2VecModel.findSynonyms("developer", 5)
// +-----------+------------------+
// |       word|        similarity|
// +-----------+------------------+
// |        dev| 0.881394624710083|
// |development|0.7730562090873718|
// |       oier|0.6866029500961304|
// |  develover|0.6720684766769409|
// |     webdev|0.6582568883895874|
// +-----------+------------------+

ref:
https://spark.apache.org/docs/latest/ml-features.html#word2vec

Calculate the Pearson Correlation between Features

import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.linalg.Matrix
import org.apache.spark.ml.stat.Correlation
import org.apache.spark.sql.Row

val featureNames = Array("stargazers_count", "forks_count", "subscribers_count")
val vectorAssembler = new VectorAssembler()
  .setInputCols(featureNames)
  .setOutputCol("features")

val df = vectorAssembler.transform(rawRepoInfoDS)
val correlationDF = Correlation.corr(df, "features")
val Row(coeff: Matrix) = correlationDF.head

println(featureNames.mkString(", "))
println(coeff.toString)
// stargazers_count, forks_count, subscribers_count
// 1.0                 0.5336901230713282  0.7664204175159971  
// 0.5336901230713282  1.0                 0.5414244966152617  
// 0.7664204175159971  0.5414244966152617  1.0

ref:
https://spark.apache.org/docs/latest/ml-statistics.html

DIMSUM

import org.apache.spark.mllib.linalg.distributed.{CoordinateMatrix, MatrixEntry}

val repoWordRDD = repoVectorDF
  .select($"repo_id", $"text_w2v")
  .rdd
  .flatMap((row: Row) => {
    val repoId = row.getInt(0)
    val vector = row.getAs[DenseVector](1)
    vector.toArray.zipWithIndex.map({
      case (element, index) => MatrixEntry(repoId, index, element)
    })
  })
val repoWordMatrix = new CoordinateMatrix(repoWordRDD)
val wordRepoMatrix = repoWordMatrix.transpose

val repoSimilarityRDD = wordRepoMatrix
  .toRowMatrix
  .columnSimilarities(0.1)
  .entries
  .flatMap({
    case MatrixEntry(row: Long, col: Long, sim: Double) => {
      if (sim >= 0.5) {
        Array((row, col, sim))
      }
      else {
        None
      }
    }
  })
spark.createDataFrame(repoSimilarityRDD).toDF("item_1", "item_2", "similarity")
repoSimilarityDF.show(false)

ref:
https://stackoverflow.com/questions/42455725/columnsimilarities-back-to-spark-data-frame
https://forums.databricks.com/questions/248/when-should-i-use-rowmatrixcolumnsimilarities.html

Train a Locality Sensitive Hashing (LSH) Model: Bucketed Random Projection LSH

To specify the value of bucketLength, if input vectors are normalized, 1-10 times of pow(numRecords, -1/inputDim) would be a reasonable value. For instance, Math.pow(334913.0, -1.0 / 200.0) = 0.9383726472256705.

import org.apache.spark.ml.feature.BucketedRandomProjectionLSH
import org.apache.spark.ml.linalg.Vectors

val userDF = spark.createDataFrame(Seq(
  (1, Vectors.sparse(6, Seq((0, -4.0), (1, 1.0), (2, 0.2)))),
  (2, Vectors.sparse(6, Seq((0, 5.5), (1, -0.6), (2, 9.0)))),
  (3, Vectors.sparse(6, Seq((1, 1.0), (2, 5.3), (4, 3.0)))),
  (4, Vectors.sparse(6, Seq((1, 1.0), (2, 1.0), (4, 1.0)))),
  (5, Vectors.sparse(6, Seq((2, 1.0), (5, -0.2)))),
  (6, Vectors.sparse(6, Seq((0, 0.7)))),
  (7, Vectors.sparse(6, Seq((1, 0.3), (2, 1.0))))
)).toDF("user_id", "features")

val repoDF = spark.createDataFrame(Seq(
  (11, Vectors.sparse(6, Seq((0, 1.0), (1, 1.0), (2, 1.0), (3, 1.0), (4, 1.0), (5, 1.0)))),
  (12, Vectors.sparse(6, Seq((0, 9.0), (1, -2.0), (2, -21.0), (3, 9.0), (4, 1.0), (5, 9.0)))),
  (13, Vectors.sparse(6, Seq((0, 1.0), (1, 1.0), (2, -3.0), (3, 3.0), (4, 7.0), (5, 9.0)))),
  (14, Vectors.sparse(6, Seq((0, 1.0), (1, 1.0), (2, -3.0)))),
  (15, Vectors.sparse(6, Seq((1, 1.0), (2, 1.0))))
)).toDF("repo_id", "features")

val lsh = new BucketedRandomProjectionLSH()
  .setBucketLength(0.6812920690579612)
  .setNumHashTables(4)
  .setInputCol("features")
  .setOutputCol("hashes")
val lshModel = lsh.fit(repoDF)

val hashedUserDF = lshModel.transform(userDF)
val hashedRepoDF = lshModel.transform(repoDF)
hashedRepoDF.show(false)
// +-------+----------------------------------------------+--------------------------------+
// |repo_id|features                                      |hashes                          |
// +-------+----------------------------------------------+--------------------------------+
// |11     |(6,[0,1,2,3,4,5],[1.0,1.0,1.0,1.0,1.0,1.0])   |[[1.0], [-2.0], [-1.0], [-1.0]] |
// |12     |(6,[0,1,2,3,4,5],[9.0,-2.0,-21.0,9.0,1.0,9.0])|[[21.0], [-28.0], [18.0], [0.0]]|
// |13     |(6,[0,1,2,3,4,5],[1.0,1.0,-3.0,3.0,7.0,9.0])  |[[4.0], [-10.0], [6.0], [-3.0]] |
// |14     |(6,[0,1,2],[1.0,1.0,-3.0])                    |[[2.0], [-3.0], [2.0], [1.0]]   |
// |15     |(6,[1,2],[1.0,1.0])                           |[[-1.0], [0.0], [-2.0], [0.0]]  |
// +-------+----------------------------------------------+--------------------------------+

val similarDF = lshModel
  .approxSimilarityJoin(hashedUserDF, hashedRepoDF, 10.0, "distance")
  .select($"datasetA.user_id".alias("user_id"), $"datasetB.repo_id".alias("repo_id"), $"distance")
  .orderBy($"user_id", $"distance".asc)
similarDF.show(false)
// +-------+-------+------------------+
// |user_id|repo_id|distance          |
// +-------+-------+------------------+
// |1      |15     |4.079215610874228 |
// |3      |15     |5.243090691567332 |
// |4      |15     |1.0               |
// |4      |11     |1.7320508075688772|
// |5      |15     |1.019803902718557 |
// |5      |11     |2.33238075793812  |
// |6      |15     |1.57797338380595  |
// |7      |15     |0.7               |
// |7      |11     |2.118962010041709 |
// +-------+-------+------------------+

val userVector = Vectors.sparse(6, Seq((0, 1.5), (1, 0.8), (2, 2.0)))
val singleSimilarDF = lshModel
  .approxNearestNeighbors(hashedRepoDF, userVector, 5, "distance")
  .select($"repo_id", $"features", $"distance")
singleSimilarDF.show(false)
// +-------+----------------------------------------------+------------------+
// |repo_id|features                                      |distance          |
// +-------+----------------------------------------------+------------------+
// |15     |(6,[1,2],[1.0,1.0])                           |1.8138357147217055|
// |12     |(6,[0,1,2,3,4,5],[9.0,-2.0,-21.0,9.0,1.0,9.0])|27.49709075520536 |
// +-------+----------------------------------------------+------------------+

The problem of approxSimilarityJoin() is that you can't control the number of generated items, the disadvantage of approxNearestNeighbors() is that you have to manually iterate all users to find similar items. Moreover, both methods can easily suffer from the infamous java.lang.OutOfMemoryError.

ref:
https://spark.apache.org/docs/latest/ml-features.html#locality-sensitive-hashing

Train a Locality Sensitive Hashing (LSH) Model: MinHash LSH

MinHash LSH treats input as a binary vector, that is, all non-zero values (include negative values) are just 1. Basically, the Word2Vec vector won't be an appropriate input to MinHash LSH.

import org.apache.spark.ml.feature.MinHashLSH
import org.apache.spark.ml.linalg.Vectors

val userDF = spark.createDataFrame(Seq(
  (1, Vectors.sparse(6, Seq((0, -4.0), (1, 1.0), (2, 0.2)))),
  (2, Vectors.sparse(6, Seq((0, 5.5), (1, -0.6), (2, 9.0)))),
  (3, Vectors.sparse(6, Seq((1, 1.0), (2, 5.3), (4, 3.0)))),
  (4, Vectors.sparse(6, Seq((1, 1.0), (2, 1.0), (4, 1.0)))),
  (5, Vectors.sparse(6, Seq((2, 1.0), (5, -0.2)))),
  (6, Vectors.sparse(6, Seq((2, 0.7)))),
  (7, Vectors.sparse(6, Seq((3, 0.3), (5, 1.0))))
)).toDF("user_id", "features")

val repoDF = spark.createDataFrame(Seq(
  (11, Vectors.sparse(6, Seq((1, 1.0), (3, 1.0), (5, 1.0)))),
  (12, Vectors.sparse(6, Seq((2, 1.0), (3, 1.0), (5, 1.0)))),
  (13, Vectors.sparse(6, Seq((1, 1.0), (2, 1.0), (4, 1.0))))
)).toDF("repo_id", "features")

val lsh = new MinHashLSH()
  .setNumHashTables(4)
  .setInputCol("features")
  .setOutputCol("hashes")

val lshModel = lsh.fit(userDF)
val hashedUserDF = lshModel.transform(userDF)
val hashedRepoDF = lshModel.transform(repoDF)

hashedUserDF.show(false)
// user 1 and 2 have the same hashed vector
// user 3 and 4 have the same hashed vector
// +-------+--------------------------+-----------------------------------------------------------------------+
// |user_id|features                  |hashes                                                                 |
// +-------+--------------------------+-----------------------------------------------------------------------+
// |1      |(6,[0,1,2],[-4.0,1.0,0.2])|[[-2.031299587E9], [-1.974869772E9], [-1.974047307E9], [4.95314097E8]] |
// |2      |(6,[0,1,2],[5.5,-0.6,9.0])|[[-2.031299587E9], [-1.974869772E9], [-1.974047307E9], [4.95314097E8]] |
// |3      |(6,[1,2,4],[1.0,5.3,3.0]) |[[-2.031299587E9], [-1.974869772E9], [-1.230128022E9], [8.7126731E8]]  |
// |4      |(6,[1,2,4],[1.0,1.0,1.0]) |[[-2.031299587E9], [-1.974869772E9], [-1.230128022E9], [8.7126731E8]]  |
// |5      |(6,[2,5],[1.0,-0.2])      |[[-2.031299587E9], [-1.758749518E9], [-4.86208737E8], [-1.919887134E9]]|
// |6      |(6,[2],[0.7])             |[[-2.031299587E9], [-1.758749518E9], [-4.86208737E8], [1.247220523E9]] |
// |7      |(6,[3,5],[0.3,1.0])       |[[-1.278435698E9], [-1.542629264E9], [2.57710548E8], [-1.919887134E9]] |
// +-------+--------------------------+-----------------------------------------------------------------------+

val userSimilarRepoDF = lshModel
  .approxSimilarityJoin(hashedUserDF, hashedRepoDF, 0.6, "distance")
  .select($"datasetA.user_id".alias("user_id"), $"datasetB.repo_id".alias("repo_id"), $"distance")
  .orderBy($"user_id", $"distance".asc)

userSimilarRepoDF.show(false)
// +-------+-------+-------------------+
// |user_id|repo_id|distance           |
// +-------+-------+-------------------+
// |1      |13     |0.5                |
// |2      |13     |0.5                |
// |3      |13     |0.0                |
// |4      |13     |0.0                |
// |5      |12     |0.33333333333333337|
// |7      |12     |0.33333333333333337|
// |7      |11     |0.33333333333333337|
// +-------+-------+-------------------+

ref:
https://databricks.com/blog/2017/05/09/detecting-abuse-scale-locality-sensitive-hashing-uber-engineering.html

Train a Logistic Regression Model

import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.linalg.Vectors

val training = spark.createDataFrame(Seq(
  (1.0, Vectors.dense(1.0, 2.5, 0.0, 0.0)),
  (1.0, Vectors.dense(0.1, 9.0, 0.0, 0.0)),
  (1.0, Vectors.dense(0.0, 0.0, 1.0, 0.0)),
  (0.0, Vectors.dense(0.0, 0.0, 2.0, 9.0)),
  (0.0, Vectors.dense(1.0, 0.0, 0.0, 5.0))
)).toDF("label", "features")

val lr = new LogisticRegression()
  .setMaxIter(100)
  .setRegParam(0.0)
  .setElasticNetParam(0.0)
  .setFamily("binomial")
  .setFeaturesCol("features")
  .setLabelCol("label")

lr.explainParams()

val lrModel = lr.fit(training)

println(s"Coefficients: ${lrModel.coefficients}")
// [2.0149015925419,2.694173163503675,9.547978766053463,-5.592221425156231]

println(s"Intercept: ${lrModel.intercept}")
// 8.552229795281482

val result = lrModel.transform(test)

ref:
https://spark.apache.org/docs/latest/ml-classification-regression.html#logistic-regression
https://spark.apache.org/docs/latest/mllib-linear-methods.html#logistic-regression

import org.apache.spark.ml.classification.BinaryLogisticRegressionSummary

val binarySummary = lrModel.summary.asInstanceOf[BinaryLogisticRegressionSummary]
println(s"Area Under ROC: ${binarySummary.areaUnderROC}")

ref:
https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary

Evaluate a Binary Classification Model

import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
import org.apache.spark.ml.linalg.Vectors

val df = spark.createDataFrame(Seq(
  (Vectors.dense(0.0, 2.5), 1.0), // correct
  (Vectors.dense(1.0, 4.1), 1.0), // correct
  (Vectors.dense(9.2, 1.1), 0.0), // correct
  (Vectors.dense(1.0, 0.1), 0.0), // correct
  (Vectors.dense(5.0, 0.5), 1.0)  // incorrect
)).toDF("rawPrediction", "starring")

val evaluator = new BinaryClassificationEvaluator()
  .setMetricName("areaUnderROC")
  .setRawPredictionCol("rawPrediction")
  .setLabelCol("starring")
val metric = evaluator.evaluate(df)
// 0.8333333333333333

ref:
https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.ml.evaluation.BinaryClassificationEvaluator

Train an ALS Model

import org.apache.spark.ml.recommendation.ALS

val df = spark.createDataFrame(Seq(
  (1, 1, 12),
  (1, 2, 90),
  (1, 4, 4),
  (2, 4, 1),
  (3, 5, 8)
)).toDF("user", "item", "rating")

val als = new ALS()
  .setImplicitPrefs(true)
  .setRank(5)
  .setRegParam(0.5)
  .setAlpha(40)
  .setMaxIter(10)
  .setSeed(42)
  .setColdStartStrategy("drop")
val alsModel = als.fit(df)

val predictionDF = alsModel.transform(df)
// +----+----+------+----------+
// |user|item|rating|prediction|
// +----+----+------+----------+
// |   1|   1|    12| 0.9988487|
// |   3|   5|     8| 0.9984464|
// |   1|   4|     4|0.99887615|
// |   2|   4|     1| 0.9921428|
// |   1|   2|    90| 0.9997897|
// +----+----+------+----------+

predictionDF.printSchema()
// root
 // |-- user: integer (nullable = false)
 // |-- item: integer (nullable = false)
 // |-- rating: integer (nullable = false)
// |-- prediction: float (nullable = false)

val userRecommendationsDF = alsModel.recommendForAllUsers(15)
// +----+-----------------------------------------------------------------+
// |user|recommendations                                                  |
// +----+-----------------------------------------------------------------+
// |1   |[[2,0.9997897], [4,0.9988761], [1,0.9988487], [5,0.0]]           |
// |3   |[[5,0.9984464], [1,2.9802322E-8], [2,0.0], [4,0.0]]              |
// |2   |[[4,0.9921428], [2,0.10759391], [1,0.10749264], [5,1.4901161E-8]]|
// +----+-----------------------------------------------------------------+

userRecommendationsDF.printSchema()
// root
 // |-- user: integer (nullable = false)
 // |-- recommendations: array (nullable = true)
 // |    |-- element: struct (containsNull = true)
 // |    |    |-- item: integer (nullable = true)
// |    |    |-- rating: float (nullable = true)

ref:
https://spark.apache.org/docs/latest/ml-collaborative-filtering.html

Save and Load an ALS Model

import org.apache.hadoop.mapred.InvalidInputException
import org.apache.spark.ml.recommendation.{ALS, ALSModel}

val alsModelSavePath = "./spark-data/20170902/alsModel.parquet"
val alsModel: ALSModel = try {
  ALSModel.load(alsModelSavePath)
} catch {
  case e: InvalidInputException => {
    if (e.getMessage().contains("Input path does not exist")) {
      val als = new ALS()
        .setImplicitPrefs(true)
        .setRank(100)
        .setRegParam(0.5)
        .setAlpha(40)
        .setMaxIter(22)
        .setSeed(42)
        .setColdStartStrategy("drop")
        .setUserCol("user_id")
        .setItemCol("repo_id")
        .setRatingCol("starring")
      val alsModel = als.fit(rawRepoStarringDS)
      alsModel.save(alsModelSavePath)
      alsModel
    } else {
      throw e
    }
  }
}

Create a Custom Transformer

package ws.vinta.albedo.transformers

import org.apache.spark.broadcast.Broadcast
import org.apache.spark.ml.Transformer
import org.apache.spark.ml.param.{DoubleParam, Param, ParamMap}
import org.apache.spark.ml.util.{DefaultParamsWritable, Identifiable}
import org.apache.spark.sql.types._
import org.apache.spark.sql.{DataFrame, Dataset, Row}

import scala.collection.mutable

class NegativeBalancer(override val uid: String, val bcPopularItems: Broadcast[mutable.LinkedHashSet[Int]])
  extends Transformer with DefaultParamsWritable {

  def this(bcPopularItems: Broadcast[mutable.LinkedHashSet[Int]]) = {
    this(Identifiable.randomUID("negativeBalancer"), bcPopularItems)
  }

  val userCol = new Param[String](this, "userCol", "User 所在的欄位名稱")

  def getUserCol: String = $(userCol)

  def setUserCol(value: String): this.type = set(userCol, value)
  setDefault(userCol -> "user")

  val itemCol = new Param[String](this, "itemCol", "Item 所在的欄位名稱")

  def getItemCol: String = $(itemCol)

  def setItemCol(value: String): this.type = set(itemCol, value)
  setDefault(itemCol -> "item")

  val labelCol = new Param[String](this, "labelCol", "Label 所在的欄位名稱")

  def getLabelCol: String = $(labelCol)

  def setLabelCol(value: String): this.type = set(labelCol, value)
  setDefault(labelCol -> "label")

  val negativeValue = new DoubleParam(this, "negativeValue", "負樣本的值")

  def getNegativeValue: Double = $(negativeValue)

  def setNegativeValue(value: Double): this.type = set(negativeValue, value)
  setDefault(negativeValue -> 0.0)

  val negativePositiveRatio = new DoubleParam(this, "negativePositiveRatio", "負樣本與正樣本的比例")

  def getNegativePositiveRatio: Double = $(negativePositiveRatio)

  def setNegativePositiveRatio(value: Double): this.type = set(negativePositiveRatio, value)
  setDefault(negativePositiveRatio -> 1.0)

  override def transformSchema(schema: StructType): StructType = {
    Map($(userCol) -> IntegerType, $(itemCol) -> IntegerType, $(labelCol) -> DoubleType)
      .foreach{
        case(columnName: String, expectedDataType: DataType) => {
          val actualDataType = schema(columnName).dataType
          require(actualDataType.equals(IntegerType), s"Column $columnName must be of type $expectedDataType but was actually $actualDataType.")
        }
      }

    schema
  }

  override def transform(dataset: Dataset[_]): DataFrame = {
    transformSchema(dataset.schema)

    val popularItems: mutable.LinkedHashSet[Int] = this.bcPopularItems.value

    val emptyItemSet = new mutable.HashSet[Int]
    val addToItemSet = (itemSet: mutable.HashSet[Int], item: Int) => itemSet += item
    val mergeItemSets = (set1: mutable.HashSet[Int], set2: mutable.HashSet[Int]) => set1 ++= set2

    val getUserNegativeItems = (userItemsPair: (Int, mutable.HashSet[Int])) => {
      val (user, positiveItems) = userItemsPair
      val negativeItems = popularItems.diff(positiveItems)
      val requiredNegativeItemsCount = (positiveItems.size * this.getNegativePositiveRatio).toInt
      (user, negativeItems.slice(0, requiredNegativeItemsCount))
    }
    val expandNegativeItems = (userItemsPair: (Int, mutable.LinkedHashSet[Int])) => {
      val (user, negativeItems) = userItemsPair
      negativeItems.map({(user, _, $(negativeValue))})
    }

    import dataset.sparkSession.implicits._

    // TODO: 目前是假設傳進來的 dataset 都是 positive samples，之後可能得處理含有 negative samples 的情況
    val negativeDF = dataset
      .select($(userCol), $(itemCol))
      .rdd
      .map({
        case Row(user: Int, item: Int) => (user, item)
      })
      .aggregateByKey(emptyItemSet)(addToItemSet, mergeItemSets)
      .map(getUserNegativeItems)
      .flatMap(expandNegativeItems)
      .toDF($(userCol), $(itemCol), $(labelCol))

    dataset.select($(userCol), $(itemCol), $(labelCol)).union(negativeDF)
  }

  override def copy(extra: ParamMap): this.type = {
    defaultCopy(extra)
  }
}

ref:
https://www.safaribooksonline.com/library/view/high-performance-spark/9781491943199/ch09.html#extending_spark_ml
https://stackoverflow.com/questions/40615713/how-to-write-a-custom-transformer-in-mllib
https://issues.apache.org/jira/browse/SPARK-17048

Create a Custom Evaluator

package ws.vinta.albedo.evaluators

import org.apache.spark.ml.evaluation.Evaluator
import org.apache.spark.ml.param.{Param, ParamMap}
import org.apache.spark.ml.util.{DefaultParamsWritable, Identifiable}
import org.apache.spark.mllib.evaluation.RankingMetrics
import org.apache.spark.sql.{DataFrame, Dataset, Row}

class RankingEvaluator(override val uid: String, val userActualItemsDF: DataFrame)
  extends Evaluator with DefaultParamsWritable {

  def this(userActualItemsDF: DataFrame) = {
    this(Identifiable.randomUID("rankingEvaluator"), userActualItemsDF)
  }

  val metricName = new Param[String](this, "metricName", "評估方式")

  def getMetricName: String = $(metricName)

  def setMetricName(value: String): this.type = set(metricName, value)
  setDefault(metricName -> "ndcg@k")

  val k = new Param[Int](this, "k", "只評估前 k 個 items 的排序結果")

  def getK: Int = $(k)

  def setK(value: Int): this.type = set(k, value)
  setDefault(k -> 15)

  override def isLargerBetter: Boolean = $(metricName) match {
    case "map" => true
    case "ndcg@k" => true
    case "precision@k" => true
  }

  override def evaluate(dataset: Dataset[_]): Double = {
    import dataset.sparkSession.implicits._

    val userPredictedItemsDF = dataset.select($"user_id", $"recommendations.repo_id".alias("items"))

    val bothItemsRDD = userPredictedItemsDF.join(userActualItemsDF, Seq("user_id", "user_id"))
      .select(userPredictedItemsDF.col("items"), userActualItemsDF.col("items"))
      .rdd
      .map((row: Row) => {
        // Row(userPredictedItems, userActualItems)
        (row(0).asInstanceOf[Seq[Int]].toArray, row(1).asInstanceOf[Seq[Int]].toArray)
      })

    val rankingMetrics = new RankingMetrics(bothItemsRDD)
    val metric = $(metricName) match {
      case "map" => rankingMetrics.meanAveragePrecision
      case "ndcg@k" => rankingMetrics.ndcgAt($(k))
      case "precision@k" => rankingMetrics.precisionAt($(k))
    }
    metric
  }

  override def copy(extra: ParamMap): RankingEvaluator = {
    defaultCopy(extra)
  }
}

ref:
https://spark.apache.org/docs/latest/mllib-evaluation-metrics.html#ranking-systems
https://www.safaribooksonline.com/library/view/spark-the-definitive/9781491912201/ch19.html#s6c5---recommendation

Apply Transformer on Multiple Columns

import org.apache.spark.ml.feature._

val userCategoricalColumnNames = Array("account_type", "clean_company", "clean_email", "clean_location")
val userCategoricalTransformers = userCategoricalColumnNames.flatMap((columnName: String) => {
  val stringIndexer = new StringIndexer()
    .setInputCol(columnName)
    .setOutputCol(s"${columnName}_index")
    .setHandleInvalid("keep")
  val oneHotEncoder = new OneHotEncoder()
    .setInputCol(s"${columnName}_index")
    .setOutputCol(s"${columnName}_ohe")
    .setDropLast(true)
  Array(stringIndexer, oneHotEncoder)
})
userCategoricalTransformers.foreach(println)
// strIdx_4029f57e379a
// oneHot_f0decb92a05c
// strIdx_fb855ad6caaa
// oneHot_f1be19344002
// strIdx_7fa62a683293
// oneHot_097ae442d8fc
// strIdx_0ff7ffa022a1
// oneHot_4a9f72a7f5d8

ref:
https://stackoverflow.com/questions/34167105/using-spark-mls-onehotencoder-on-multiple-columns

Cross-validate a Pipeline Model

import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.tuning.{CrossValidator, ParamGridBuilder}

val vectorAssembler = new VectorAssembler()
  .setInputCols(Array("feature1", "feature2", "feature3"))
  .setOutputCol("features")

val lr = new LogisticRegression()
  .setFeaturesCol("features")
  .setLabelCol("starring")

val pipeline = new Pipeline()
  .setStages(Array(vectorAssembler, lr))

val paramGrid = new ParamGridBuilder()
  .addGrid(lr.maxIter, Array(20, 100))
  .addGrid(lr.regParam, Array(0.0, 0.5, 1.0, 2.0))
  .addGrid(lr.elasticNetParam, Array(0.0, 0.5, 1.0))
  .build()

val evaluator = new BinaryClassificationEvaluator()
  .setMetricName("areaUnderROC")
  .setRawPredictionCol("rawPrediction")
  .setLabelCol("starring")

val cv = new CrossValidator()
  .setEstimator(pipeline)
  .setEstimatorParamMaps(paramGrid)
  .setEvaluator(evaluator)
  .setNumFolds(3)

val cvModel = cv.fit(trainingDF)

ref:
https://spark.apache.org/docs/latest/ml-tuning.html#cross-validation

Extract Best Parameters from a Cross-validation Model

import org.apache.spark.ml.PipelineModel
import org.apache.spark.ml.classification.LogisticRegressionModel

val bestPipelineModel = cvModel.bestModel.asInstanceOf[PipelineModel]
val lrModel = bestPipelineModel.stages(0).asInstanceOf[LogisticRegressionModel]
lrModel.extractParamMap()
// or
lrModel.explainParams()

ref:
https://stackoverflow.com/questions/31749593/how-to-extract-best-parameters-from-a-crossvalidatormodel

Show All Parameters of a Cross-validation Model

import org.apache.spark.ml.param.ParamMap

cvModel.getEstimatorParamMaps
  .zip(cvModel.avgMetrics)
  .sortWith(_._2 > _._2)
  .foreach((pair: (ParamMap, Double)) => {
    println(s"${pair._2}: ${pair._1}")
  })
// 0.8999999999999999: {
//     hashingTF_ac8be8d5806b-numFeatures: 1000,
//     logreg_9f79de6e51ec-regParam: 0.1
// }
// 0.8875: {
//     hashingTF_ac8be8d5806b-numFeatures: 100,
//     logreg_9f79de6e51ec-regParam: 0.1
// }
// 0.875: {
//     hashingTF_ac8be8d5806b-numFeatures: 100,
//     logreg_9f79de6e51ec-regParam: 0.01
// }

ref:
https://stackoverflow.com/questions/31749593/how-to-extract-best-parameters-from-a-crossvalidatormodel
https://alvinalexander.com/scala/how-sort-scala-sequences-seq-list-array-buffer-vector-ordering-ordered