fix session recommender (intel-analytics#1558)

dding3 · Aug 8, 2019 · 54943c0 · 54943c0
1 parent f1a7940
commit 54943c0
Showing 1 changed file with 16 additions and 24 deletions.
diff --git a/scala/com/intel/analytics/zoo/models/recommendation/SessionRecommenderSpec.scala b/scala/com/intel/analytics/zoo/models/recommendation/SessionRecommenderSpec.scala
@@ -52,7 +52,7 @@ class SessionRecommenderSpec extends ZooSpecHelper {
 
     val itemCount = 100
     val sessionLength = 10
-    val model = SessionRecommender[Float](itemCount, sessionLength, includeHistory = false)
+    val model = SessionRecommender[Float](itemCount, sessionLength = sessionLength)
     val ran = new Random(42L)
     val data = (1 to 100).map { x =>
       val items: Seq[Float] = for (i <- 1 to sessionLength) yield
@@ -69,7 +69,7 @@ class SessionRecommenderSpec extends ZooSpecHelper {
     val itemCount = 100
     val sessionLength = 10
     val historyLength = 5
-    val model = SessionRecommender[Float](itemCount, sessionLength,
+    val model = SessionRecommender[Float](itemCount, sessionLength = sessionLength,
       includeHistory = true, historyLength = historyLength)
     val ran = new Random(42L)
     val data = (1 to 100).map { x =>
@@ -91,26 +91,10 @@ class SessionRecommenderSpec extends ZooSpecHelper {
     val itemCount = 100
     val sessionLength = 10
     val historyLength = 5
-    val model = SessionRecommender[Float](itemCount, sessionLength,
+    val model = SessionRecommender[Float](itemCount, sessionLength = sessionLength,
       includeHistory = true, historyLength = historyLength)
     val ran = new Random(42L)
-    val data1: RDD[Sample[Float]] = sc.parallelize(1 to 100)
-      .map { x =>
-        val items1: Seq[Float] = for (i <- 1 to sessionLength) yield ran.nextInt(itemCount).toFloat
-        val items2: Seq[Float] = for (i <- 1 to historyLength) yield ran.nextInt(itemCount).toFloat
-        val input1 = Tensor(items1.toArray, Array(sessionLength))
-        val input2 = Tensor(items2.toArray, Array(historyLength))
-        Sample[Float](Array(input1, input2))
-      }
-
-    val recommedations1 = model.recommendForSession(data1, 3, zeroBasedLabel = false)
-    recommedations1.take(10)
-      .map { x =>
-        assert(x.size == 3)
-        assert(x(0)._2 >= x(1)._2)
-      }
-
-    val data2: Array[Sample[Float]] = (1 to 10)
+    val data1: Array[Sample[Float]] = (1 to 10)
       .map { x =>
         val items1: Seq[Float] = for (i <- 1 to sessionLength) yield ran.nextInt(itemCount).toFloat
         val items2: Seq[Float] = for (i <- 1 to historyLength) yield ran.nextInt(itemCount).toFloat
@@ -119,19 +103,26 @@ class SessionRecommenderSpec extends ZooSpecHelper {
         Sample[Float](Array(input1, input2))
       }.toArray
 
-    val recommedations2 = model.recommendForSession(data2, 4, zeroBasedLabel = false)
-    recommedations2.map { x =>
+    val recommedations1 = model.recommendForSession(data1, 4, zeroBasedLabel = false)
+    recommedations1.map { x =>
       assert(x.size == 4)
       assert(x(0)._2 >= x(1)._2)
     }
+
+    val data2: RDD[Sample[Float]] = sc.parallelize(data1)
+    val recommedations2 = model.recommendForSession(data2, 3, zeroBasedLabel = false)
+    recommedations2.take(10).map { x =>
+      assert(x.size == 3)
+      assert(x(0)._2 >= x(1)._2)
+    }
   }
 
   "SessionRecommender compile and fit" should "work properly" in {
 
     val itemCount = 100
     val sessionLength = 10
     val historyLength = 5
-    val model = SessionRecommender[Float](itemCount, sessionLength,
+    val model = SessionRecommender[Float](itemCount, 10, sessionLength = sessionLength,
       includeHistory = true, historyLength = historyLength)
     val ran = new Random(42L)
     val data1 = sc.parallelize(1 to 100)
@@ -144,6 +135,7 @@ class SessionRecommenderSpec extends ZooSpecHelper {
         Sample(Array(input1, input2), Array(label))
       }
     model.compile(optimizer = "rmsprop", loss = "sparse_categorical_crossentropy")
+    model.summary()
     model.fit(data1, nbEpoch = 1)
   }
 }
@@ -153,7 +145,7 @@ class SessionRecommenderSerialTest extends ModuleSerializationTest {
     val ran = new Random(42L)
     val itemCount = 100
     val sessionLength = 10
-    val model = SessionRecommender[Float](100, 10, includeHistory = false)
+    val model = SessionRecommender[Float](100, sessionLength = 10)
     val items: Seq[Float] = for (i <- 1 to sessionLength) yield
       ran.nextInt(itemCount - 1).toFloat + 1
     val data = Tensor(items.toArray, Array(sessionLength)).resize(1, sessionLength)