Re: org.apache.spark.ml.recommendation.ALS
Hi Xiangrui,
Here is the class:
object ALSNew {
def main (args: Array[String]) {
val conf = new SparkConf()
.setAppName(TrainingDataPurchase)
.set(spark.executor.memory, 4g)
conf.set(spark.shuffle.memoryFraction,0.65) //default is 0.2
conf.set(spark.storage.memoryFraction,0.3)//default is 0.6
val sc = new SparkContext(conf)
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext.implicits._
val pfile = args(0)
val purchase=sc.textFile(pfile)
val ratings = purchase.map ( line =
line.split(',') match { case Array(user, item, rate) =
(user.toInt, item.toInt, rate.toFloat)
}).toDF()
val rank = args(1).toInt
val numIterations = args(2).toInt
val regParam : Double = 0.01
val implicitPrefs : Boolean = true
val numUserBlocks : Int = 100
val numItemBlocks : Int = 100
val nonnegative : Boolean = true
//val paramMap = ParamMap (regParam=0.01)
//paramMap.put(numUserBlocks=100, numItemBlocks=100)
val als = new ALS()
.setRank(rank)
.setRegParam(regParam)
.setImplicitPrefs(implicitPrefs)
.setNumUserBlocks(numUserBlocks)
.setNumItemBlocks(numItemBlocks)
val alpha = als.getAlpha
val model = als.fit(ratings)
val predictions = model.transform(ratings)
.select(rating, prediction)
.map { case Row(rating: Float, prediction: Float) =
(rating.toDouble, prediction.toDouble)
}
val rmse =
if (implicitPrefs) {
// TODO: Use a better (rank-based?) evaluation metric for implicit
feedback.
// We limit the ratings and the predictions to interval [0, 1] and
compute the weighted RMSE
// with the confidence scores as weights.
val (totalWeight, weightedSumSq) = predictions.map { case (rating,
prediction) =
val confidence = 1.0 + alpha * math.abs(rating)
val rating01 = math.max(math.min(rating, 1.0), 0.0)
val prediction01 = math.max(math.min(prediction, 1.0), 0.0)
val err = prediction01 - rating01
(confidence, confidence * err * err)
}.reduce { case ((c0, e0), (c1, e1)) =
(c0 + c1, e0 + e1)
}
math.sqrt(weightedSumSq /totalWeight)
} else {
val mse = predictions.map { case (rating, prediction) =
val err = rating - prediction
err * err
}.mean()
math.sqrt(mse)
}
println(Mean Squared Error = + rmse)
}
}
I am using the following in my maven build (pom.xml):
dependencies
dependency
groupIdorg.scala-lang/groupId
artifactIdscala-library/artifactId
version2.11.2/version
/dependency
dependency
groupIdorg.apache.spark/groupId
artifactIdspark-core_2.11/artifactId
version1.3.0/version
/dependency
dependency
groupIdorg.apache.spark/groupId
artifactIdspark-mllib_2.11/artifactId
version1.3.0/version
/dependency
dependency
groupIdorg.apache.spark/groupId
artifactIdspark-sql_2.11/artifactId
version1.3.0/version
/dependency
/dependencies
I am using scala version 2.11.2.
Could it be that spark-1.3.0-bin-hadoop2.4.tgz requires a different version
of scala ?
Thanks,
Jay
On Apr 9, 2015, at 4:38 PM, Xiangrui Meng men...@gmail.com wrote:
Could you share ALSNew.scala? Which Scala version did you use? -Xiangrui
On Wed, Apr 8, 2015 at 4:09 PM, Jay Katukuri jkatuk...@apple.com wrote:
Hi Xiangrui,
I tried running this on my local machine (laptop) and got the same error:
Here is what I did:
1. downloaded spark 1.30 release version (prebuilt for hadoop 2.4 and later)
spark-1.3.0-bin-hadoop2.4.tgz.
2. Ran the following command:
spark-submit --class ALSNew --master local[8] ALSNew.jar /input_path
The stack trace is exactly same.
Thanks,
Jay
On Apr 8, 2015, at 10:47 AM, Jay Katukuri jkatuk...@apple.com wrote:
some additional context:
Since, I am using features of spark 1.3.0, I have downloaded spark 1.3.0 and
used spark-submit from there.
The cluster is still on spark-1.2.0.
So, this looks to me that at runtime, the executors could not find some
libraries of spark-1.3.0, even though I ran spark-submit from my downloaded
spark-1.30.
On Apr 6, 2015, at 1:37 PM, Jay Katukuri jkatuk...@apple.com wrote:
Here is the command that I have used :
spark-submit —class packagename.ALSNew --num-executors 100 --master yarn
ALSNew.jar -jar spark-sql_2.11-1.3.0.jar hdfs://input_path
Btw - I could run the old ALS in mllib package.
On Apr 6, 2015, at 12:32 PM, Xiangrui Meng men...@gmail.com wrote:
So ALSNew.scala is your own application, did you add it with
spark-submit or spark-shell? The correct command should like
spark-submit --class your.package.name.ALSNew
Re: org.apache.spark.ml.recommendation.ALS
some additional context:
Since, I am using features of spark 1.3.0, I have downloaded spark 1.3.0 and
used spark-submit from there.
The cluster is still on spark-1.2.0.
So, this looks to me that at runtime, the executors could not find some
libraries of spark-1.3.0, even though I ran spark-submit from my downloaded
spark-1.30.
On Apr 6, 2015, at 1:37 PM, Jay Katukuri jkatuk...@apple.com wrote:
Here is the command that I have used :
spark-submit —class packagename.ALSNew --num-executors 100 --master yarn
ALSNew.jar -jar spark-sql_2.11-1.3.0.jar hdfs://input_path
Btw - I could run the old ALS in mllib package.
On Apr 6, 2015, at 12:32 PM, Xiangrui Meng men...@gmail.com wrote:
So ALSNew.scala is your own application, did you add it with
spark-submit or spark-shell? The correct command should like
spark-submit --class your.package.name.ALSNew ALSNew.jar [options]
Please check the documentation:
http://spark.apache.org/docs/latest/submitting-applications.html
-Xiangrui
On Mon, Apr 6, 2015 at 12:27 PM, Jay Katukuri jkatuk...@apple.com wrote:
Hi,
Here is the stack trace:
Exception in thread main java.lang.NoSuchMethodError:
scala.reflect.api.JavaUniverse.runtimeMirror(Ljava/lang/ClassLoader;)Lscala/reflect/api/JavaUniverse$JavaMirror;
at ALSNew$.main(ALSNew.scala:35)
at ALSNew.main(ALSNew.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:483)
at
org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:569)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:166)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:189)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:110)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Thanks,
Jay
On Apr 6, 2015, at 12:24 PM, Xiangrui Meng men...@gmail.com wrote:
Please attach the full stack trace. -Xiangrui
On Mon, Apr 6, 2015 at 12:06 PM, Jay Katukuri jkatuk...@apple.com wrote:
Hi all,
I got a runtime error while running the ALS.
Exception in thread main java.lang.NoSuchMethodError:
scala.reflect.api.JavaUniverse.runtimeMirror(Ljava/lang/ClassLoader;)Lscala/reflect/api/JavaUniverse$JavaMirror;
The error that I am getting is at the following code:
val ratings = purchase.map ( line =
line.split(',') match { case Array(user, item, rate) =
(user.toInt, item.toInt, rate.toFloat)
}).toDF()
Any help is appreciated !
I have tried passing the spark-sql jar using the -jar
spark-sql_2.11-1.3.0.jar
Thanks,
Jay
On Mar 17, 2015, at 12:50 PM, Xiangrui Meng men...@gmail.com wrote:
Please remember to copy the user list next time. I might not be able
to respond quickly. There are many others who can help or who can
benefit from the discussion. Thanks! -Xiangrui
On Tue, Mar 17, 2015 at 12:04 PM, Jay Katukuri jkatuk...@apple.com wrote:
Great Xiangrui. It works now.
Sorry that I needed to bug you :)
Jay
On Mar 17, 2015, at 11:48 AM, Xiangrui Meng men...@gmail.com wrote:
Please check this section in the user guide:
http://spark.apache.org/docs/latest/sql-programming-guide.html#inferring-the-schema-using-reflection
You need `import sqlContext.implicits._` to use `toDF()`.
-Xiangrui
On Mon, Mar 16, 2015 at 2:34 PM, Jay Katukuri jkatuk...@apple.com wrote:
Hi Xiangrui,
Thanks a lot for the quick reply.
I am still facing an issue.
I have tried the code snippet that you have suggested:
val ratings = purchase.map { line =
line.split(',') match { case Array(user, item, rate) =
(user.toInt, item.toInt, rate.toFloat)
}.toDF(user, item, rate”)}
for this, I got the below error:
error: ';' expected but '.' found.
[INFO] }.toDF(user, item, rate”)}
[INFO] ^
when I tried below code
val ratings = purchase.map ( line =
line.split(',') match { case Array(user, item, rate) =
(user.toInt, item.toInt, rate.toFloat)
}).toDF(user, item, rate)
error: value toDF is not a member of org.apache.spark.rdd.RDD[(Int, Int,
Float)]
[INFO] possible cause: maybe a semicolon is missing before `value toDF'?
[INFO] }).toDF(user, item, rate)
I have looked at the document that you have shared and tried the following
code:
case class Record(user: Int, item: Int, rate:Double)
val ratings = purchase.map(_.split(',')).map(r =Record(r(0).toInt,
r(1).toInt, r(2).toDouble)) .toDF(user, item, rate)
for this, I got the below error:
error: value toDF is not a member of org.apache.spark.rdd.RDD[Record]
Appreciate your help !
Thanks,
Jay
On Mar 16, 2015, at 11:35 AM, Xiangrui Meng men...@gmail.com wrote:
Try this:
val ratings = purchase.map { line =
line.split
Re: org.apache.spark.ml.recommendation.ALS
Here is the command that I have used :
spark-submit —class packagename.ALSNew --num-executors 100 --master yarn
ALSNew.jar -jar spark-sql_2.11-1.3.0.jar hdfs://input_path
Btw - I could run the old ALS in mllib package.
On Apr 6, 2015, at 12:32 PM, Xiangrui Meng men...@gmail.com wrote:
So ALSNew.scala is your own application, did you add it with
spark-submit or spark-shell? The correct command should like
spark-submit --class your.package.name.ALSNew ALSNew.jar [options]
Please check the documentation:
http://spark.apache.org/docs/latest/submitting-applications.html
-Xiangrui
On Mon, Apr 6, 2015 at 12:27 PM, Jay Katukuri jkatuk...@apple.com wrote:
Hi,
Here is the stack trace:
Exception in thread main java.lang.NoSuchMethodError:
scala.reflect.api.JavaUniverse.runtimeMirror(Ljava/lang/ClassLoader;)Lscala/reflect/api/JavaUniverse$JavaMirror;
at ALSNew$.main(ALSNew.scala:35)
at ALSNew.main(ALSNew.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:483)
at
org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:569)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:166)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:189)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:110)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Thanks,
Jay
On Apr 6, 2015, at 12:24 PM, Xiangrui Meng men...@gmail.com wrote:
Please attach the full stack trace. -Xiangrui
On Mon, Apr 6, 2015 at 12:06 PM, Jay Katukuri jkatuk...@apple.com wrote:
Hi all,
I got a runtime error while running the ALS.
Exception in thread main java.lang.NoSuchMethodError:
scala.reflect.api.JavaUniverse.runtimeMirror(Ljava/lang/ClassLoader;)Lscala/reflect/api/JavaUniverse$JavaMirror;
The error that I am getting is at the following code:
val ratings = purchase.map ( line =
line.split(',') match { case Array(user, item, rate) =
(user.toInt, item.toInt, rate.toFloat)
}).toDF()
Any help is appreciated !
I have tried passing the spark-sql jar using the -jar
spark-sql_2.11-1.3.0.jar
Thanks,
Jay
On Mar 17, 2015, at 12:50 PM, Xiangrui Meng men...@gmail.com wrote:
Please remember to copy the user list next time. I might not be able
to respond quickly. There are many others who can help or who can
benefit from the discussion. Thanks! -Xiangrui
On Tue, Mar 17, 2015 at 12:04 PM, Jay Katukuri jkatuk...@apple.com wrote:
Great Xiangrui. It works now.
Sorry that I needed to bug you :)
Jay
On Mar 17, 2015, at 11:48 AM, Xiangrui Meng men...@gmail.com wrote:
Please check this section in the user guide:
http://spark.apache.org/docs/latest/sql-programming-guide.html#inferring-the-schema-using-reflection
You need `import sqlContext.implicits._` to use `toDF()`.
-Xiangrui
On Mon, Mar 16, 2015 at 2:34 PM, Jay Katukuri jkatuk...@apple.com wrote:
Hi Xiangrui,
Thanks a lot for the quick reply.
I am still facing an issue.
I have tried the code snippet that you have suggested:
val ratings = purchase.map { line =
line.split(',') match { case Array(user, item, rate) =
(user.toInt, item.toInt, rate.toFloat)
}.toDF(user, item, rate”)}
for this, I got the below error:
error: ';' expected but '.' found.
[INFO] }.toDF(user, item, rate”)}
[INFO] ^
when I tried below code
val ratings = purchase.map ( line =
line.split(',') match { case Array(user, item, rate) =
(user.toInt, item.toInt, rate.toFloat)
}).toDF(user, item, rate)
error: value toDF is not a member of org.apache.spark.rdd.RDD[(Int, Int,
Float)]
[INFO] possible cause: maybe a semicolon is missing before `value toDF'?
[INFO] }).toDF(user, item, rate)
I have looked at the document that you have shared and tried the following
code:
case class Record(user: Int, item: Int, rate:Double)
val ratings = purchase.map(_.split(',')).map(r =Record(r(0).toInt,
r(1).toInt, r(2).toDouble)) .toDF(user, item, rate)
for this, I got the below error:
error: value toDF is not a member of org.apache.spark.rdd.RDD[Record]
Appreciate your help !
Thanks,
Jay
On Mar 16, 2015, at 11:35 AM, Xiangrui Meng men...@gmail.com wrote:
Try this:
val ratings = purchase.map { line =
line.split(',') match { case Array(user, item, rate) =
(user.toInt, item.toInt, rate.toFloat)
}.toDF(user, item, rate)
Doc for DataFrames:
http://spark.apache.org/docs/latest/sql-programming-guide.html
-Xiangrui
On Mon, Mar 16, 2015 at 9:08 AM, jaykatukuri jkatuk...@apple.com wrote:
Hi all,
I am trying to use the new ALS implementation under
org.apache.spark.ml.recommendation.ALS.
The new method
org.apache.spark.ml.recommendation.ALS
Hi all,
I got a runtime error while running the ALS.
Exception in thread main java.lang.NoSuchMethodError:
scala.reflect.api.JavaUniverse.runtimeMirror(Ljava/lang/ClassLoader;)Lscala/reflect/api/JavaUniverse$JavaMirror;
The error that I am getting is at the following code:
val ratings = purchase.map ( line =
line.split(',') match { case Array(user, item, rate) =
(user.toInt, item.toInt, rate.toFloat)
}).toDF()
Any help is appreciated !
I have tried passing the spark-sql jar using the -jar spark-sql_2.11-1.3.0.jar
Thanks,
Jay
On Mar 17, 2015, at 12:50 PM, Xiangrui Meng men...@gmail.com wrote:
Please remember to copy the user list next time. I might not be able
to respond quickly. There are many others who can help or who can
benefit from the discussion. Thanks! -Xiangrui
On Tue, Mar 17, 2015 at 12:04 PM, Jay Katukuri jkatuk...@apple.com wrote:
Great Xiangrui. It works now.
Sorry that I needed to bug you :)
Jay
On Mar 17, 2015, at 11:48 AM, Xiangrui Meng men...@gmail.com wrote:
Please check this section in the user guide:
http://spark.apache.org/docs/latest/sql-programming-guide.html#inferring-the-schema-using-reflection
You need `import sqlContext.implicits._` to use `toDF()`.
-Xiangrui
On Mon, Mar 16, 2015 at 2:34 PM, Jay Katukuri jkatuk...@apple.com wrote:
Hi Xiangrui,
Thanks a lot for the quick reply.
I am still facing an issue.
I have tried the code snippet that you have suggested:
val ratings = purchase.map { line =
line.split(',') match { case Array(user, item, rate) =
(user.toInt, item.toInt, rate.toFloat)
}.toDF(user, item, rate”)}
for this, I got the below error:
error: ';' expected but '.' found.
[INFO] }.toDF(user, item, rate”)}
[INFO] ^
when I tried below code
val ratings = purchase.map ( line =
line.split(',') match { case Array(user, item, rate) =
(user.toInt, item.toInt, rate.toFloat)
}).toDF(user, item, rate)
error: value toDF is not a member of org.apache.spark.rdd.RDD[(Int, Int,
Float)]
[INFO] possible cause: maybe a semicolon is missing before `value toDF'?
[INFO] }).toDF(user, item, rate)
I have looked at the document that you have shared and tried the following
code:
case class Record(user: Int, item: Int, rate:Double)
val ratings = purchase.map(_.split(',')).map(r =Record(r(0).toInt,
r(1).toInt, r(2).toDouble)) .toDF(user, item, rate)
for this, I got the below error:
error: value toDF is not a member of org.apache.spark.rdd.RDD[Record]
Appreciate your help !
Thanks,
Jay
On Mar 16, 2015, at 11:35 AM, Xiangrui Meng men...@gmail.com wrote:
Try this:
val ratings = purchase.map { line =
line.split(',') match { case Array(user, item, rate) =
(user.toInt, item.toInt, rate.toFloat)
}.toDF(user, item, rate)
Doc for DataFrames:
http://spark.apache.org/docs/latest/sql-programming-guide.html
-Xiangrui
On Mon, Mar 16, 2015 at 9:08 AM, jaykatukuri jkatuk...@apple.com wrote:
Hi all,
I am trying to use the new ALS implementation under
org.apache.spark.ml.recommendation.ALS.
The new method to invoke for training seems to be override def
fit(dataset:
DataFrame, paramMap: ParamMap): ALSModel.
How do I create a dataframe object from ratings data set that is on hdfs ?
where as the method in the old ALS implementation under
org.apache.spark.mllib.recommendation.ALS was
def train(
ratings: RDD[Rating],
rank: Int,
iterations: Int,
lambda: Double,
blocks: Int,
seed: Long
): MatrixFactorizationModel
My code to run the old ALS train method is as below:
val sc = new SparkContext(conf)
val pfile = args(0)
val purchase=sc.textFile(pfile)
val ratings = purchase.map(_.split(',') match { case Array(user, item,
rate) =
Rating(user.toInt, item.toInt, rate.toInt)
})
val model = ALS.train(ratings, rank, numIterations, 0.01)
Now, for the new ALS fit method, I am trying to use the below code to run,
but getting a compilation error:
val als = new ALS()
.setRank(rank)
.setRegParam(regParam)
.setImplicitPrefs(implicitPrefs)
.setNumUserBlocks(numUserBlocks)
.setNumItemBlocks(numItemBlocks)
val sc = new SparkContext(conf)
val pfile = args(0)
val purchase=sc.textFile(pfile)
val ratings = purchase.map(_.split(',') match { case Array(user, item,
rate) =
Rating(user.toInt, item.toInt, rate.toInt)
})
val model = als.fit(ratings.toDF())
I get an error that the method toDF() is not a member of
org.apache.spark.rdd.RDD[org.apache.spark.ml.recommendation.ALS.Rating[Int]].
Appreciate the help !
Thanks,
Jay
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/RDD-to-DataFrame-for-using-ALS-under-org-apache-spark-ml-recommendation-ALS-tp22083.html
Sent from the Apache Spark User List mailing list archive at Nabble.com
Re: org.apache.spark.ml.recommendation.ALS
Hi,
Here is the stack trace:
Exception in thread main java.lang.NoSuchMethodError:
scala.reflect.api.JavaUniverse.runtimeMirror(Ljava/lang/ClassLoader;)Lscala/reflect/api/JavaUniverse$JavaMirror;
at ALSNew$.main(ALSNew.scala:35)
at ALSNew.main(ALSNew.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:483)
at
org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:569)
at
org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:166)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:189)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:110)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Thanks,
Jay
On Apr 6, 2015, at 12:24 PM, Xiangrui Meng men...@gmail.com wrote:
Please attach the full stack trace. -Xiangrui
On Mon, Apr 6, 2015 at 12:06 PM, Jay Katukuri jkatuk...@apple.com wrote:
Hi all,
I got a runtime error while running the ALS.
Exception in thread main java.lang.NoSuchMethodError:
scala.reflect.api.JavaUniverse.runtimeMirror(Ljava/lang/ClassLoader;)Lscala/reflect/api/JavaUniverse$JavaMirror;
The error that I am getting is at the following code:
val ratings = purchase.map ( line =
line.split(',') match { case Array(user, item, rate) =
(user.toInt, item.toInt, rate.toFloat)
}).toDF()
Any help is appreciated !
I have tried passing the spark-sql jar using the -jar
spark-sql_2.11-1.3.0.jar
Thanks,
Jay
On Mar 17, 2015, at 12:50 PM, Xiangrui Meng men...@gmail.com wrote:
Please remember to copy the user list next time. I might not be able
to respond quickly. There are many others who can help or who can
benefit from the discussion. Thanks! -Xiangrui
On Tue, Mar 17, 2015 at 12:04 PM, Jay Katukuri jkatuk...@apple.com wrote:
Great Xiangrui. It works now.
Sorry that I needed to bug you :)
Jay
On Mar 17, 2015, at 11:48 AM, Xiangrui Meng men...@gmail.com wrote:
Please check this section in the user guide:
http://spark.apache.org/docs/latest/sql-programming-guide.html#inferring-the-schema-using-reflection
You need `import sqlContext.implicits._` to use `toDF()`.
-Xiangrui
On Mon, Mar 16, 2015 at 2:34 PM, Jay Katukuri jkatuk...@apple.com wrote:
Hi Xiangrui,
Thanks a lot for the quick reply.
I am still facing an issue.
I have tried the code snippet that you have suggested:
val ratings = purchase.map { line =
line.split(',') match { case Array(user, item, rate) =
(user.toInt, item.toInt, rate.toFloat)
}.toDF(user, item, rate”)}
for this, I got the below error:
error: ';' expected but '.' found.
[INFO] }.toDF(user, item, rate”)}
[INFO] ^
when I tried below code
val ratings = purchase.map ( line =
line.split(',') match { case Array(user, item, rate) =
(user.toInt, item.toInt, rate.toFloat)
}).toDF(user, item, rate)
error: value toDF is not a member of org.apache.spark.rdd.RDD[(Int, Int,
Float)]
[INFO] possible cause: maybe a semicolon is missing before `value toDF'?
[INFO] }).toDF(user, item, rate)
I have looked at the document that you have shared and tried the following
code:
case class Record(user: Int, item: Int, rate:Double)
val ratings = purchase.map(_.split(',')).map(r =Record(r(0).toInt,
r(1).toInt, r(2).toDouble)) .toDF(user, item, rate)
for this, I got the below error:
error: value toDF is not a member of org.apache.spark.rdd.RDD[Record]
Appreciate your help !
Thanks,
Jay
On Mar 16, 2015, at 11:35 AM, Xiangrui Meng men...@gmail.com wrote:
Try this:
val ratings = purchase.map { line =
line.split(',') match { case Array(user, item, rate) =
(user.toInt, item.toInt, rate.toFloat)
}.toDF(user, item, rate)
Doc for DataFrames:
http://spark.apache.org/docs/latest/sql-programming-guide.html
-Xiangrui
On Mon, Mar 16, 2015 at 9:08 AM, jaykatukuri jkatuk...@apple.com wrote:
Hi all,
I am trying to use the new ALS implementation under
org.apache.spark.ml.recommendation.ALS.
The new method to invoke for training seems to be override def fit(dataset:
DataFrame, paramMap: ParamMap): ALSModel.
How do I create a dataframe object from ratings data set that is on hdfs ?
where as the method in the old ALS implementation under
org.apache.spark.mllib.recommendation.ALS was
def train(
ratings: RDD[Rating],
rank: Int,
iterations: Int,
lambda: Double,
blocks: Int,
seed: Long
): MatrixFactorizationModel
My code to run the old ALS train method is as below:
val sc = new SparkContext(conf)
val pfile = args(0)
val purchase=sc.textFile(pfile)
val ratings = purchase.map
