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[GitHub] [spark] uchiiii opened a new pull request, #36920: [MLLIB] Modify constructor of RankingMetrics class
uchiiii opened a new pull request, #36920:
URL: https://github.com/apache/spark/pull/36920
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### What changes were proposed in this pull request?
- Merged the two constructor into one using `RDD[_ <: Product]`.
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### Why are the changes needed?
- To make code simpler.
- To support even more inputs.
- The previous code treats `rel` as an empty array when `rel` is not provided, which is not that beautiful. This change removes this.
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### Does this PR introduce _any_ user-facing change?
NO
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[GitHub] [spark] uchiiii commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
uchiiii commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r903175108
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -154,54 +151,68 @@ class RankingMetrics[T: ClassTag] @Since("3.4.0") (
@Since("1.2.0")
def ndcgAt(k: Int): Double = {
require(k > 0, "ranking position k should be positive")
- predictionAndLabels.map { case (pred, lab, rel) =>
- val useBinary = rel.isEmpty
- val labSet = lab.toSet
- val relMap = lab.zip(rel).toMap
- if (useBinary && lab.size != rel.size) {
- logWarning(
- "# of ground truth set and # of relevance value set should be equal, " +
- "check input data")
- }
-
- if (labSet.nonEmpty) {
- val labSetSize = labSet.size
- val n = math.min(math.max(pred.length, labSetSize), k)
- var maxDcg = 0.0
- var dcg = 0.0
- var i = 0
- while (i < n) {
- if (useBinary) {
- // Base of the log doesn't matter for calculating NDCG,
- // if the relevance value is binary.
- val gain = 1.0 / math.log(i + 2)
- if (i < pred.length && labSet.contains(pred(i))) {
- dcg += gain
- }
- if (i < labSetSize) {
- maxDcg += gain
+ predictionAndLabels
+ .map {
+ case (pred: Array[T], lab: Array[T]) =>
Review Comment:
@zhengruifeng
Sorry to interrupt you, but which do you think is better?
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[GitHub] [spark] srowen closed pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
srowen closed pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
URL: https://github.com/apache/spark/pull/36920
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[GitHub] [spark] uchiiii commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
uchiiii commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r903175108
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -154,54 +151,68 @@ class RankingMetrics[T: ClassTag] @Since("3.4.0") (
@Since("1.2.0")
def ndcgAt(k: Int): Double = {
require(k > 0, "ranking position k should be positive")
- predictionAndLabels.map { case (pred, lab, rel) =>
- val useBinary = rel.isEmpty
- val labSet = lab.toSet
- val relMap = lab.zip(rel).toMap
- if (useBinary && lab.size != rel.size) {
- logWarning(
- "# of ground truth set and # of relevance value set should be equal, " +
- "check input data")
- }
-
- if (labSet.nonEmpty) {
- val labSetSize = labSet.size
- val n = math.min(math.max(pred.length, labSetSize), k)
- var maxDcg = 0.0
- var dcg = 0.0
- var i = 0
- while (i < n) {
- if (useBinary) {
- // Base of the log doesn't matter for calculating NDCG,
- // if the relevance value is binary.
- val gain = 1.0 / math.log(i + 2)
- if (i < pred.length && labSet.contains(pred(i))) {
- dcg += gain
- }
- if (i < labSetSize) {
- maxDcg += gain
+ predictionAndLabels
+ .map {
+ case (pred: Array[T], lab: Array[T]) =>
Review Comment:
@zhengruifeng
Sorry for interrupting you, but which do you think is better?
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[GitHub] [spark] uchiiii commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
uchiiii commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r901533321
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -70,9 +62,14 @@ class RankingMetrics[T: ClassTag] @Since("3.4.0") (
@Since("1.2.0")
def precisionAt(k: Int): Double = {
require(k > 0, "ranking position k should be positive")
- predictionAndLabels.map { case (pred, lab, _) =>
- countRelevantItemRatio(pred, lab, k, k)
- }.mean()
+ predictionAndLabels
Review Comment:
Do you mean that we use `rdd` as a private variable whose type is `RDD[(Array[T], Array[T], Array[Double])]`, and keep other methods almost the same ?
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -70,9 +62,14 @@ class RankingMetrics[T: ClassTag] @Since("3.4.0") (
@Since("1.2.0")
def precisionAt(k: Int): Double = {
require(k > 0, "ranking position k should be positive")
- predictionAndLabels.map { case (pred, lab, _) =>
- countRelevantItemRatio(pred, lab, k, k)
- }.mean()
+ predictionAndLabels
Review Comment:
Do you mean that we use `rdd` as a private variable whose type is `RDD[(Array[T], Array[T], Array[Double])]`, and keep other methods almost the same?
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[GitHub] [spark] uchiiii commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
uchiiii commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r902047346
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -154,54 +151,68 @@ class RankingMetrics[T: ClassTag] @Since("3.4.0") (
@Since("1.2.0")
def ndcgAt(k: Int): Double = {
require(k > 0, "ranking position k should be positive")
- predictionAndLabels.map { case (pred, lab, rel) =>
- val useBinary = rel.isEmpty
- val labSet = lab.toSet
- val relMap = lab.zip(rel).toMap
- if (useBinary && lab.size != rel.size) {
- logWarning(
- "# of ground truth set and # of relevance value set should be equal, " +
- "check input data")
- }
-
- if (labSet.nonEmpty) {
- val labSetSize = labSet.size
- val n = math.min(math.max(pred.length, labSetSize), k)
- var maxDcg = 0.0
- var dcg = 0.0
- var i = 0
- while (i < n) {
- if (useBinary) {
- // Base of the log doesn't matter for calculating NDCG,
- // if the relevance value is binary.
- val gain = 1.0 / math.log(i + 2)
- if (i < pred.length && labSet.contains(pred(i))) {
- dcg += gain
- }
- if (i < labSetSize) {
- maxDcg += gain
+ predictionAndLabels
+ .map {
+ case (pred: Array[T], lab: Array[T]) =>
Review Comment:
Which do you think is better for `ndcgAt`?
- The previous one, where to use binary is decided based on whether `rel` is an empty array.
- The current one, where to use binary is decided based on user input directly.
IMHO, the current one is easier to understand.
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[GitHub] [spark] uchiiii commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
uchiiii commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r906175522
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -38,16 +38,14 @@ import org.apache.spark.rdd.RDD
* Since 3.4.0, it supports ndcg evaluation with relevance value.
*/
@Since("1.2.0")
-class RankingMetrics[T: ClassTag] @Since("3.4.0") (
- predictionAndLabels: RDD[(Array[T], Array[T], Array[Double])])
+class RankingMetrics[T: ClassTag] @Since("1.2.0") (predictionAndLabels: RDD[_ <: Product])
extends Logging
with Serializable {
- @Since("1.2.0")
- def this(predictionAndLabelsWithoutRelevance: => RDD[(Array[T], Array[T])]) = {
- this(predictionAndLabelsWithoutRelevance.map {
- case (pred, lab) => (pred, lab, Array.empty[Double])
- })
+ private val rdd = predictionAndLabels.map {
Review Comment:
How about `fullRDD`?
IMO, Names like `predictionsAndLabelsAndRelevances` are too long.
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r906621586
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -38,16 +38,14 @@ import org.apache.spark.rdd.RDD
* Since 3.4.0, it supports ndcg evaluation with relevance value.
*/
@Since("1.2.0")
-class RankingMetrics[T: ClassTag] @Since("3.4.0") (
- predictionAndLabels: RDD[(Array[T], Array[T], Array[Double])])
+class RankingMetrics[T: ClassTag] @Since("1.2.0") (predictionAndLabels: RDD[_ <: Product])
extends Logging
with Serializable {
- @Since("1.2.0")
- def this(predictionAndLabelsWithoutRelevance: => RDD[(Array[T], Array[T])]) = {
- this(predictionAndLabelsWithoutRelevance.map {
- case (pred, lab) => (pred, lab, Array.empty[Double])
- })
+ private val rdd = predictionAndLabels.map {
Review Comment:
maybe `predictionsLabelsRelevances`?
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[GitHub] [spark] uchiiii commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
uchiiii commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r903607449
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -154,54 +151,68 @@ class RankingMetrics[T: ClassTag] @Since("3.4.0") (
@Since("1.2.0")
def ndcgAt(k: Int): Double = {
require(k > 0, "ranking position k should be positive")
- predictionAndLabels.map { case (pred, lab, rel) =>
- val useBinary = rel.isEmpty
- val labSet = lab.toSet
- val relMap = lab.zip(rel).toMap
- if (useBinary && lab.size != rel.size) {
- logWarning(
- "# of ground truth set and # of relevance value set should be equal, " +
- "check input data")
- }
-
- if (labSet.nonEmpty) {
- val labSetSize = labSet.size
- val n = math.min(math.max(pred.length, labSetSize), k)
- var maxDcg = 0.0
- var dcg = 0.0
- var i = 0
- while (i < n) {
- if (useBinary) {
- // Base of the log doesn't matter for calculating NDCG,
- // if the relevance value is binary.
- val gain = 1.0 / math.log(i + 2)
- if (i < pred.length && labSet.contains(pred(i))) {
- dcg += gain
- }
- if (i < labSetSize) {
- maxDcg += gain
+ predictionAndLabels
+ .map {
+ case (pred: Array[T], lab: Array[T]) =>
Review Comment:
Thank you for your opinion.
Hm, I thought the current one may be more concise for the developers because you could easily understand the calculation process is different by input type (even though I wrote both of them).
Anyway, I changed this to the previous one.
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[GitHub] [spark] uchiiii commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
uchiiii commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r903171942
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -38,16 +38,14 @@ import org.apache.spark.rdd.RDD
* Since 3.4.0, it supports ndcg evaluation with relevance value.
*/
@Since("1.2.0")
-class RankingMetrics[T: ClassTag] @Since("3.4.0") (
- predictionAndLabels: RDD[(Array[T], Array[T], Array[Double])])
+class RankingMetrics[T: ClassTag] @Since("1.2.0") (predictionAndLabels: RDD[_ <: Product])
extends Logging
with Serializable {
- @Since("1.2.0")
- def this(predictionAndLabelsWithoutRelevance: => RDD[(Array[T], Array[T])]) = {
- this(predictionAndLabelsWithoutRelevance.map {
- case (pred, lab) => (pred, lab, Array.empty[Double])
- })
+ private val rdd = predictionAndLabels.map {
Review Comment:
We may want not to change the name because
- `MulticlassMetrics` also has the arguments whose name is `predictionAndLabels`.
https://github.com/apache/spark/blob/b588d070ebc234280af730c8f9915e8859b1886e/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala#L28-L35
- We change the interface of the constructor to be able to support more inputs. Specific names like `predictionAndLabelsWithOptionalRelevance` may go against the goal.
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[GitHub] [spark] srowen commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
srowen commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r902609854
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -38,16 +38,14 @@ import org.apache.spark.rdd.RDD
* Since 3.4.0, it supports ndcg evaluation with relevance value.
*/
@Since("1.2.0")
-class RankingMetrics[T: ClassTag] @Since("3.4.0") (
- predictionAndLabels: RDD[(Array[T], Array[T], Array[Double])])
+class RankingMetrics[T: ClassTag] @Since("1.2.0") (predictionAndLabels: RDD[_ <: Product])
extends Logging
with Serializable {
- @Since("1.2.0")
- def this(predictionAndLabelsWithoutRelevance: => RDD[(Array[T], Array[T])]) = {
- this(predictionAndLabelsWithoutRelevance.map {
- case (pred, lab) => (pred, lab, Array.empty[Double])
- })
+ private val rdd = predictionAndLabels.map {
Review Comment:
Maybe we can rename this and the constructor arg; the constructor arg may also have relevance; this does not
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[GitHub] [spark] uchiiii commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
uchiiii commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r902047346
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -154,54 +151,68 @@ class RankingMetrics[T: ClassTag] @Since("3.4.0") (
@Since("1.2.0")
def ndcgAt(k: Int): Double = {
require(k > 0, "ranking position k should be positive")
- predictionAndLabels.map { case (pred, lab, rel) =>
- val useBinary = rel.isEmpty
- val labSet = lab.toSet
- val relMap = lab.zip(rel).toMap
- if (useBinary && lab.size != rel.size) {
- logWarning(
- "# of ground truth set and # of relevance value set should be equal, " +
- "check input data")
- }
-
- if (labSet.nonEmpty) {
- val labSetSize = labSet.size
- val n = math.min(math.max(pred.length, labSetSize), k)
- var maxDcg = 0.0
- var dcg = 0.0
- var i = 0
- while (i < n) {
- if (useBinary) {
- // Base of the log doesn't matter for calculating NDCG,
- // if the relevance value is binary.
- val gain = 1.0 / math.log(i + 2)
- if (i < pred.length && labSet.contains(pred(i))) {
- dcg += gain
- }
- if (i < labSetSize) {
- maxDcg += gain
+ predictionAndLabels
+ .map {
+ case (pred: Array[T], lab: Array[T]) =>
Review Comment:
Which do you think is better for `ndcgAt`?
- The previous one, where to use `binary` is decided based on whether `rel` is an empty array.
- The current one, where to use `binary` is decided based on user input directly.
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[GitHub] [spark] srowen commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
srowen commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r906167565
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -38,16 +38,14 @@ import org.apache.spark.rdd.RDD
* Since 3.4.0, it supports ndcg evaluation with relevance value.
*/
@Since("1.2.0")
-class RankingMetrics[T: ClassTag] @Since("3.4.0") (
- predictionAndLabels: RDD[(Array[T], Array[T], Array[Double])])
+class RankingMetrics[T: ClassTag] @Since("1.2.0") (predictionAndLabels: RDD[_ <: Product])
extends Logging
with Serializable {
- @Since("1.2.0")
- def this(predictionAndLabelsWithoutRelevance: => RDD[(Array[T], Array[T])]) = {
- this(predictionAndLabelsWithoutRelevance.map {
- case (pred, lab) => (pred, lab, Array.empty[Double])
- })
+ private val rdd = predictionAndLabels.map {
Review Comment:
WDYT @uchiiii ?
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[GitHub] [spark] uchiiii commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
uchiiii commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r903175108
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -154,54 +151,68 @@ class RankingMetrics[T: ClassTag] @Since("3.4.0") (
@Since("1.2.0")
def ndcgAt(k: Int): Double = {
require(k > 0, "ranking position k should be positive")
- predictionAndLabels.map { case (pred, lab, rel) =>
- val useBinary = rel.isEmpty
- val labSet = lab.toSet
- val relMap = lab.zip(rel).toMap
- if (useBinary && lab.size != rel.size) {
- logWarning(
- "# of ground truth set and # of relevance value set should be equal, " +
- "check input data")
- }
-
- if (labSet.nonEmpty) {
- val labSetSize = labSet.size
- val n = math.min(math.max(pred.length, labSetSize), k)
- var maxDcg = 0.0
- var dcg = 0.0
- var i = 0
- while (i < n) {
- if (useBinary) {
- // Base of the log doesn't matter for calculating NDCG,
- // if the relevance value is binary.
- val gain = 1.0 / math.log(i + 2)
- if (i < pred.length && labSet.contains(pred(i))) {
- dcg += gain
- }
- if (i < labSetSize) {
- maxDcg += gain
+ predictionAndLabels
+ .map {
+ case (pred: Array[T], lab: Array[T]) =>
Review Comment:
@zhengruifeng　@srowen
Sorry to interrupt you, but which do you think is better?
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r901346286
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -70,9 +62,14 @@ class RankingMetrics[T: ClassTag] @Since("3.4.0") (
@Since("1.2.0")
def precisionAt(k: Int): Double = {
require(k > 0, "ranking position k should be positive")
- predictionAndLabels.map { case (pred, lab, _) =>
- countRelevantItemRatio(pred, lab, k, k)
- }.mean()
+ predictionAndLabels
Review Comment:
I think we may create a private rdd, and than use it internally to minimize changes.
something like this:
```
private val rdd = predictionAndLabels.map {
case (pred: Array[T], lab: Array[T]) => ...
case (pred: Array[T], lab: Array[T], rel: Array[Double]) => ...
}
```
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r901586489
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -70,9 +62,14 @@ class RankingMetrics[T: ClassTag] @Since("3.4.0") (
@Since("1.2.0")
def precisionAt(k: Int): Double = {
require(k > 0, "ranking position k should be positive")
- predictionAndLabels.map { case (pred, lab, _) =>
- countRelevantItemRatio(pred, lab, k, k)
- }.mean()
+ predictionAndLabels
Review Comment:
yes, hope this can reduce modifications
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[GitHub] [spark] uchiiii commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
uchiiii commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r902047346
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -154,54 +151,68 @@ class RankingMetrics[T: ClassTag] @Since("3.4.0") (
@Since("1.2.0")
def ndcgAt(k: Int): Double = {
require(k > 0, "ranking position k should be positive")
- predictionAndLabels.map { case (pred, lab, rel) =>
- val useBinary = rel.isEmpty
- val labSet = lab.toSet
- val relMap = lab.zip(rel).toMap
- if (useBinary && lab.size != rel.size) {
- logWarning(
- "# of ground truth set and # of relevance value set should be equal, " +
- "check input data")
- }
-
- if (labSet.nonEmpty) {
- val labSetSize = labSet.size
- val n = math.min(math.max(pred.length, labSetSize), k)
- var maxDcg = 0.0
- var dcg = 0.0
- var i = 0
- while (i < n) {
- if (useBinary) {
- // Base of the log doesn't matter for calculating NDCG,
- // if the relevance value is binary.
- val gain = 1.0 / math.log(i + 2)
- if (i < pred.length && labSet.contains(pred(i))) {
- dcg += gain
- }
- if (i < labSetSize) {
- maxDcg += gain
+ predictionAndLabels
+ .map {
+ case (pred: Array[T], lab: Array[T]) =>
Review Comment:
Which do you think is better?
- The previous one, where to use `binary` is decided based on whether `rel` is an empty array.
- The current one, where to use `binary` is decided based on user input directly.
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[GitHub] [spark] uchiiii commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
uchiiii commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r902047346
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -154,54 +151,68 @@ class RankingMetrics[T: ClassTag] @Since("3.4.0") (
@Since("1.2.0")
def ndcgAt(k: Int): Double = {
require(k > 0, "ranking position k should be positive")
- predictionAndLabels.map { case (pred, lab, rel) =>
- val useBinary = rel.isEmpty
- val labSet = lab.toSet
- val relMap = lab.zip(rel).toMap
- if (useBinary && lab.size != rel.size) {
- logWarning(
- "# of ground truth set and # of relevance value set should be equal, " +
- "check input data")
- }
-
- if (labSet.nonEmpty) {
- val labSetSize = labSet.size
- val n = math.min(math.max(pred.length, labSetSize), k)
- var maxDcg = 0.0
- var dcg = 0.0
- var i = 0
- while (i < n) {
- if (useBinary) {
- // Base of the log doesn't matter for calculating NDCG,
- // if the relevance value is binary.
- val gain = 1.0 / math.log(i + 2)
- if (i < pred.length && labSet.contains(pred(i))) {
- dcg += gain
- }
- if (i < labSetSize) {
- maxDcg += gain
+ predictionAndLabels
+ .map {
+ case (pred: Array[T], lab: Array[T]) =>
Review Comment:
Which do you think is better for `ndcgAt`?
- The previous one, where to use binary is decided based on whether `rel` is an empty array.
- The current one, where to use binary is decided based on user input directly.
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[GitHub] [spark] uchiiii commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
uchiiii commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r903607449
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -154,54 +151,68 @@ class RankingMetrics[T: ClassTag] @Since("3.4.0") (
@Since("1.2.0")
def ndcgAt(k: Int): Double = {
require(k > 0, "ranking position k should be positive")
- predictionAndLabels.map { case (pred, lab, rel) =>
- val useBinary = rel.isEmpty
- val labSet = lab.toSet
- val relMap = lab.zip(rel).toMap
- if (useBinary && lab.size != rel.size) {
- logWarning(
- "# of ground truth set and # of relevance value set should be equal, " +
- "check input data")
- }
-
- if (labSet.nonEmpty) {
- val labSetSize = labSet.size
- val n = math.min(math.max(pred.length, labSetSize), k)
- var maxDcg = 0.0
- var dcg = 0.0
- var i = 0
- while (i < n) {
- if (useBinary) {
- // Base of the log doesn't matter for calculating NDCG,
- // if the relevance value is binary.
- val gain = 1.0 / math.log(i + 2)
- if (i < pred.length && labSet.contains(pred(i))) {
- dcg += gain
- }
- if (i < labSetSize) {
- maxDcg += gain
+ predictionAndLabels
+ .map {
+ case (pred: Array[T], lab: Array[T]) =>
Review Comment:
Thank you for your opinion.
hm, I thought the current one may be more concise for the developers because you could easily understand the calculation process is different by input type (even though I wrote both of them).
Anyway, I changed this to the previous one.
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[GitHub] [spark] srowen commented on pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
srowen commented on PR #36920:
URL: https://github.com/apache/spark/pull/36920#issuecomment-1166347050
Merged to master
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r903179811
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -154,54 +151,68 @@ class RankingMetrics[T: ClassTag] @Since("3.4.0") (
@Since("1.2.0")
def ndcgAt(k: Int): Double = {
require(k > 0, "ranking position k should be positive")
- predictionAndLabels.map { case (pred, lab, rel) =>
- val useBinary = rel.isEmpty
- val labSet = lab.toSet
- val relMap = lab.zip(rel).toMap
- if (useBinary && lab.size != rel.size) {
- logWarning(
- "# of ground truth set and # of relevance value set should be equal, " +
- "check input data")
- }
-
- if (labSet.nonEmpty) {
- val labSetSize = labSet.size
- val n = math.min(math.max(pred.length, labSetSize), k)
- var maxDcg = 0.0
- var dcg = 0.0
- var i = 0
- while (i < n) {
- if (useBinary) {
- // Base of the log doesn't matter for calculating NDCG,
- // if the relevance value is binary.
- val gain = 1.0 / math.log(i + 2)
- if (i < pred.length && labSet.contains(pred(i))) {
- dcg += gain
- }
- if (i < labSetSize) {
- maxDcg += gain
+ predictionAndLabels
+ .map {
+ case (pred: Array[T], lab: Array[T]) =>
Review Comment:
I think `The previous one` maybe more concise.
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r903178123
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -38,16 +38,14 @@ import org.apache.spark.rdd.RDD
* Since 3.4.0, it supports ndcg evaluation with relevance value.
*/
@Since("1.2.0")
-class RankingMetrics[T: ClassTag] @Since("3.4.0") (
- predictionAndLabels: RDD[(Array[T], Array[T], Array[Double])])
+class RankingMetrics[T: ClassTag] @Since("1.2.0") (predictionAndLabels: RDD[_ <: Product])
extends Logging
with Serializable {
- @Since("1.2.0")
- def this(predictionAndLabelsWithoutRelevance: => RDD[(Array[T], Array[T])]) = {
- this(predictionAndLabelsWithoutRelevance.map {
- case (pred, lab) => (pred, lab, Array.empty[Double])
- })
+ private val rdd = predictionAndLabels.map {
Review Comment:
why discarding `rel` here?
If `rdd` is a `RDD[Array[T], Array[T], Array[Double]]`, then in the `ndcgAt`, we can simply check whether `rel` is empty?
also, maybe we can rename `rdd` to a more meaningful name
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[GitHub] [spark] AmplabJenkins commented on pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
AmplabJenkins commented on PR #36920:
URL: https://github.com/apache/spark/pull/36920#issuecomment-1160520565
Can one of the admins verify this patch?
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[GitHub] [spark] srowen commented on a diff in pull request #36920: [SPARK-39446][MLLIB][FOLLOWUP] Modify constructor of RankingMetrics class
Posted by GitBox <gi...@apache.org>.
srowen commented on code in PR #36920:
URL: https://github.com/apache/spark/pull/36920#discussion_r903172950
##########
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
##########
@@ -38,16 +38,14 @@ import org.apache.spark.rdd.RDD
* Since 3.4.0, it supports ndcg evaluation with relevance value.
*/
@Since("1.2.0")
-class RankingMetrics[T: ClassTag] @Since("3.4.0") (
- predictionAndLabels: RDD[(Array[T], Array[T], Array[Double])])
+class RankingMetrics[T: ClassTag] @Since("1.2.0") (predictionAndLabels: RDD[_ <: Product])
extends Logging
with Serializable {
- @Since("1.2.0")
- def this(predictionAndLabelsWithoutRelevance: => RDD[(Array[T], Array[T])]) = {
- this(predictionAndLabelsWithoutRelevance.map {
- case (pred, lab) => (pred, lab, Array.empty[Double])
- })
+ private val rdd = predictionAndLabels.map {
Review Comment:
OK that's reasonable
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