From c50f3cb6fce29fe15f8e5cdfe400844a7483bbf3 Mon Sep 17 00:00:00 2001
From: Athanasios Xygkis <athanasios.xygkis@epfl.ch>
Date: Mon, 22 Feb 2021 15:04:07 +0000
Subject: [PATCH] Json
---
README.md | 2 +-
src/main/scala/predict/Predictor.scala | 33 ++++++++++++++++++++--
src/main/scala/recommend/Recommender.scala | 19 ++++++++-----
src/main/scala/stats/Analyzer.scala | 28 ++++++++++++++++--
4 files changed, 68 insertions(+), 14 deletions(-)
diff --git a/README.md b/README.md
index aa1b7d9..51ce785 100644
--- a/README.md
+++ b/README.md
@@ -10,7 +10,7 @@ Should be available by default on the IC Cluster. Otherwise, refer to each proje
Download the ````ml-100k.zip```` dataset in the ````data/```` folder:
````
-> mkdir data
+> mkdir -p data
> cd data
> wget http://files.grouplens.org/datasets/movielens/ml-100k.zip
````
diff --git a/src/main/scala/predict/Predictor.scala b/src/main/scala/predict/Predictor.scala
index d9b871a..013030a 100644
--- a/src/main/scala/predict/Predictor.scala
+++ b/src/main/scala/predict/Predictor.scala
@@ -65,9 +65,36 @@ object Predictor extends App {
// Limiting the scope of implicit formats with {}
implicit val formats = org.json4s.DefaultFormats
val answers: Map[String, Any] = Map(
- "3.1.4" -> Map(
- "global-mae" -> globalMae
- )
+ "Q3.1.4" -> Map(
+ "MaeGlobalMethod" -> 0.0, // Datatype of answer: Double
+ "MaePerUserMethod" -> 0.0, // Datatype of answer: Double
+ "MaePerItemMethod" -> 0.0, // Datatype of answer: Double
+ "MaeBaselineMethod" -> 0.0 // Datatype of answer: Double
+ ),
+
+ "Q3.1.5" -> Map(
+ "DurationInMicrosecForGlobalMethod" -> Map(
+ "min" -> 0.0, // Datatype of answer: Double
+ "max" -> 0.0, // Datatype of answer: Double
+ "average" -> 0.0 // Datatype of answer: Double
+ ),
+ "DurationInMicrosecForPerUserMethod" -> Map(
+ "min" -> 0.0, // Datatype of answer: Double
+ "max" -> 0.0, // Datatype of answer: Double
+ "average" -> 0.0 // Datatype of answer: Double
+ ),
+ "DurationInMicrosecForPerItemMethod" -> Map(
+ "min" -> 0.0, // Datatype of answer: Double
+ "max" -> 0.0, // Datatype of answer: Double
+ "average" -> 0.0 // Datatype of answer: Double
+ ),
+ "DurationInMicrosecForBaselineMethod" -> Map(
+ "min" -> 0.0, // Datatype of answer: Double
+ "max" -> 0.0, // Datatype of answer: Double
+ "average" -> 0.0 // Datatype of answer: Double
+ ),
+ "RatioBetweenBaselineMethodAndGlobalMethod" -> 0.0 // Datatype of answer: Double
+ ),
)
json = Serialization.writePretty(answers)
}
diff --git a/src/main/scala/recommend/Recommender.scala b/src/main/scala/recommend/Recommender.scala
index 2746154..6e14620 100644
--- a/src/main/scala/recommend/Recommender.scala
+++ b/src/main/scala/recommend/Recommender.scala
@@ -61,13 +61,18 @@ object Recommender extends App {
// Limiting the scope of implicit formats with {}
implicit val formats = org.json4s.DefaultFormats
val answers: Map[String, Any] = Map(
- "4.1.1" -> List[Any](
- List(0,"Tron", 5.0),
- List(0,"Tron", 5.0),
- List(0,"Tron", 5.0),
- List(0,"Tron", 5.0),
- List(0,"Tron", 5.0)
- )
+
+ // IMPORTANT: To break ties and ensure reproducibility of results,
+ // please report the top-5 recommendations that have the smallest
+ // movie identifier.
+
+ "Q4.1.1" -> List[Any](
+ List(254, "Batman & Robin (1997)", 5.0), // Datatypes for answer: Int, String, Double
+ List(338, "Bean (1997)", 5.0),
+ List(615, "39 Steps", 5.0),
+ List(741, "Last Supper", 5.0),
+ List(587, "Hour of the Pig", 5.0)
+ )
)
json = Serialization.writePretty(answers)
}
diff --git a/src/main/scala/stats/Analyzer.scala b/src/main/scala/stats/Analyzer.scala
index 35076d5..97a9ee9 100644
--- a/src/main/scala/stats/Analyzer.scala
+++ b/src/main/scala/stats/Analyzer.scala
@@ -53,9 +53,31 @@ object Analyzer extends App {
// Limiting the scope of implicit formats with {}
implicit val formats = org.json4s.DefaultFormats
val answers: Map[String, Any] = Map(
- "3.1.1" -> Map(
- "global-avg-rating" -> 3.0
- )
+ "Q3.1.1" -> Map(
+ "GlobalAverageRating" -> 0.0 // Datatype of answer: Double
+ ),
+ "Q3.1.2" -> Map(
+ "UsersAverageRating" -> Map(
+ // Using as your input data the average rating for each user,
+ // report the min, max and average of the input data.
+ "min" -> 0.0, // Datatype of answer: Double
+ "max" -> 0.0, // Datatype of answer: Double
+ "average" -> 0.0 // Datatype of answer: Double
+ ),
+ "AllUsersCloseToGlobalAverageRating" -> true, // Datatype of answer: Boolean
+ "RatioUsersCloseToGlobalAverageRating" -> 0.0 // Datatype of answer: Double
+ ),
+ "Q3.1.3" -> Map(
+ "ItemsAverageRating" -> Map(
+ // Using as your input data the average rating for each item,
+ // report the min, max and average of the input data.
+ "min" -> 0.0, // Datatype of answer: Double
+ "max" -> 0.0, // Datatype of answer: Double
+ "average" -> 0.0 // Datatype of answer: Double
+ ),
+ "AllItemsCloseToGlobalAverageRating" -> true, // Datatype of answer: Boolean
+ "RatioItemsCloseToGlobalAverageRating" -> 0.0 // Datatype of answer: Double
+ ),
)
json = Serialization.writePretty(answers)
}
--
GitLab