[SPARK-2417][MLlib] Fix DecisionTree tests #1343
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Can one of the admins verify this patch? |
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@manishamde I see you updated the tests in #886 and used a single equality assertion for each predicted value. The tests pass either way, but in this PR I used two inequality assertions for each predicted value, like it was previously. |
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Either is fine. I think equality is a stricter constraint and would have prevented this bug. :-) |
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Jenkins, test this please. |
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@johnnywalleye Thanks for fixing this! Skipping Jenkins was totally my fault. I tested locally and the tests passed. But maybe I was on a wrong branch. |
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Jenkins, add to whitelist. |
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Merged build triggered. |
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Merged build started. |
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Merged build finished. All automated tests passed. |
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All automated tests passed. |
asfgit
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Jul 9, 2014
Fixes test failures introduced by #1316. For both the regression and classification cases, val stats is the InformationGainStats for the best tree split. stats.predict is the predicted value for the data, before the split is made. Since 600 of the 1,000 values generated by DecisionTreeSuite.generateCategoricalDataPoints() are 1.0 and the rest 0.0, the regression tree and classification tree both correctly predict a value of 0.6 for this data now, and the assertions have been changed to reflect that. Author: johnnywalleye <[email protected]> Closes #1343 from johnnywalleye/decision-tree-tests and squashes the following commits: ef80603 [johnnywalleye] [SPARK-2417][MLlib] Fix DecisionTree tests (cherry picked from commit d35e3db) Signed-off-by: Xiangrui Meng <[email protected]>
gzm55
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Jul 18, 2014
Fixes test failures introduced by apache#1316. For both the regression and classification cases, val stats is the InformationGainStats for the best tree split. stats.predict is the predicted value for the data, before the split is made. Since 600 of the 1,000 values generated by DecisionTreeSuite.generateCategoricalDataPoints() are 1.0 and the rest 0.0, the regression tree and classification tree both correctly predict a value of 0.6 for this data now, and the assertions have been changed to reflect that. Author: johnnywalleye <[email protected]> Closes apache#1343 from johnnywalleye/decision-tree-tests and squashes the following commits: ef80603 [johnnywalleye] [SPARK-2417][MLlib] Fix DecisionTree tests (cherry picked from commit d35e3db) Signed-off-by: Xiangrui Meng <[email protected]>
xiliu82
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Sep 4, 2014
Fixes test failures introduced by apache#1316. For both the regression and classification cases, val stats is the InformationGainStats for the best tree split. stats.predict is the predicted value for the data, before the split is made. Since 600 of the 1,000 values generated by DecisionTreeSuite.generateCategoricalDataPoints() are 1.0 and the rest 0.0, the regression tree and classification tree both correctly predict a value of 0.6 for this data now, and the assertions have been changed to reflect that. Author: johnnywalleye <[email protected]> Closes apache#1343 from johnnywalleye/decision-tree-tests and squashes the following commits: ef80603 [johnnywalleye] [SPARK-2417][MLlib] Fix DecisionTree tests
maropu
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Nov 17, 2020
…espect to aliases to avoid unneeded exchange/sort nodes
### What changes were proposed in this pull request?
This pull request tries to remove unneeded exchanges/sorts by normalizing the output partitioning and sortorder information correctly with respect to aliases.
Example: consider this join of three tables:
|SELECT t2id, t3.id as t3id
|FROM (
| SELECT t1.id as t1id, t2.id as t2id
| FROM t1, t2
| WHERE t1.id = t2.id
|) t12, t3
|WHERE t1id = t3.id
The plan for this looks like:
*(9) Project [t2id#1034L, id#1004L AS t3id#1035L]
+- *(9) SortMergeJoin [t1id#1033L], [id#1004L], Inner
:- *(6) Sort [t1id#1033L ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(t1id#1033L, 5), true, [id=#1343] <------------------------------
: +- *(5) Project [id#996L AS t1id#1033L, id#1000L AS t2id#1034L]
: +- *(5) SortMergeJoin [id#996L], [id#1000L], Inner
: :- *(2) Sort [id#996L ASC NULLS FIRST], false, 0
: : +- Exchange hashpartitioning(id#996L, 5), true, [id=#1329]
: : +- *(1) Range (0, 10, step=1, splits=2)
: +- *(4) Sort [id#1000L ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(id#1000L, 5), true, [id=#1335]
: +- *(3) Range (0, 20, step=1, splits=2)
+- *(8) Sort [id#1004L ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(id#1004L, 5), true, [id=#1349]
+- *(7) Range (0, 30, step=1, splits=2)
In this plan, the marked exchange could have been avoided as the data is already partitioned on "t1.id". This happens because AliasAwareOutputPartitioning class handles aliases only related to HashPartitioning. This change normalizes all output partitioning based on aliasing happening in Project.
### Why are the changes needed?
To remove unneeded exchanges.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
New UT added.
On TPCDS 1000 scale, this change improves the performance of query 95 from 330 seconds to 170 seconds by removing the extra Exchange.
Closes #30300 from prakharjain09/SPARK-33399-outputpartitioning.
Authored-by: Prakhar Jain <[email protected]>
Signed-off-by: Takeshi Yamamuro <[email protected]>
wangyum
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May 26, 2023
…rtitioning and sortorder with respect to aliases to avoid unneeded exchange/sort nodes (#1092) * [SPARK-31078][SQL] Respect aliases in output ordering Currently, in the following scenario, an unnecessary `Sort` node is introduced: ```scala withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "0") { val df = (0 until 20).toDF("i").as("df") df.repartition(8, df("i")).write.format("parquet") .bucketBy(8, "i").sortBy("i").saveAsTable("t") val t1 = spark.table("t") val t2 = t1.selectExpr("i as ii") t1.join(t2, t1("i") === t2("ii")).explain } ``` ``` == Physical Plan == *(3) SortMergeJoin [i#8], [ii#10], Inner :- *(1) Project [i#8] : +- *(1) Filter isnotnull(i#8) : +- *(1) ColumnarToRow : +- FileScan parquet default.t[i#8] Batched: true, DataFilters: [isnotnull(i#8)], Format: Parquet, Location: InMemoryFileIndex[file:/..., PartitionFilters: [], PushedFilters: [IsNotNull(i)], ReadSchema: struct<i:int>, SelectedBucketsCount: 8 out of 8 +- *(2) Sort [ii#10 ASC NULLS FIRST], false, 0 <==== UNNECESSARY +- *(2) Project [i#8 AS ii#10] +- *(2) Filter isnotnull(i#8) +- *(2) ColumnarToRow +- FileScan parquet default.t[i#8] Batched: true, DataFilters: [isnotnull(i#8)], Format: Parquet, Location: InMemoryFileIndex[file:/..., PartitionFilters: [], PushedFilters: [IsNotNull(i)], ReadSchema: struct<i:int>, SelectedBucketsCount: 8 out of 8 ``` Notice that `Sort [ii#10 ASC NULLS FIRST], false, 0` is introduced even though the underlying data is already sorted. This is because `outputOrdering` doesn't handle aliases correctly. This PR proposes to fix this issue. To better handle aliases in `outputOrdering`. Yes, now with the fix, the `explain` prints out the following: ``` == Physical Plan == *(3) SortMergeJoin [i#8], [ii#10], Inner :- *(1) Project [i#8] : +- *(1) Filter isnotnull(i#8) : +- *(1) ColumnarToRow : +- FileScan parquet default.t[i#8] Batched: true, DataFilters: [isnotnull(i#8)], Format: Parquet, Location: InMemoryFileIndex[file:/..., PartitionFilters: [], PushedFilters: [IsNotNull(i)], ReadSchema: struct<i:int>, SelectedBucketsCount: 8 out of 8 +- *(2) Project [i#8 AS ii#10] +- *(2) Filter isnotnull(i#8) +- *(2) ColumnarToRow +- FileScan parquet default.t[i#8] Batched: true, DataFilters: [isnotnull(i#8)], Format: Parquet, Location: InMemoryFileIndex[file:/..., PartitionFilters: [], PushedFilters: [IsNotNull(i)], ReadSchema: struct<i:int>, SelectedBucketsCount: 8 out of 8 ``` Tests added. Closes #27842 from imback82/alias_aware_sort_order. Authored-by: Terry Kim <[email protected]> Signed-off-by: Wenchen Fan <[email protected]> * [SPARK-33399][SQL] Normalize output partitioning and sortorder with respect to aliases to avoid unneeded exchange/sort nodes This pull request tries to remove unneeded exchanges/sorts by normalizing the output partitioning and sortorder information correctly with respect to aliases. Example: consider this join of three tables: |SELECT t2id, t3.id as t3id |FROM ( | SELECT t1.id as t1id, t2.id as t2id | FROM t1, t2 | WHERE t1.id = t2.id |) t12, t3 |WHERE t1id = t3.id The plan for this looks like: *(9) Project [t2id#1034L, id#1004L AS t3id#1035L] +- *(9) SortMergeJoin [t1id#1033L], [id#1004L], Inner :- *(6) Sort [t1id#1033L ASC NULLS FIRST], false, 0 : +- Exchange hashpartitioning(t1id#1033L, 5), true, [id=#1343] <------------------------------ : +- *(5) Project [id#996L AS t1id#1033L, id#1000L AS t2id#1034L] : +- *(5) SortMergeJoin [id#996L], [id#1000L], Inner : :- *(2) Sort [id#996L ASC NULLS FIRST], false, 0 : : +- Exchange hashpartitioning(id#996L, 5), true, [id=#1329] : : +- *(1) Range (0, 10, step=1, splits=2) : +- *(4) Sort [id#1000L ASC NULLS FIRST], false, 0 : +- Exchange hashpartitioning(id#1000L, 5), true, [id=#1335] : +- *(3) Range (0, 20, step=1, splits=2) +- *(8) Sort [id#1004L ASC NULLS FIRST], false, 0 +- Exchange hashpartitioning(id#1004L, 5), true, [id=#1349] +- *(7) Range (0, 30, step=1, splits=2) In this plan, the marked exchange could have been avoided as the data is already partitioned on "t1.id". This happens because AliasAwareOutputPartitioning class handles aliases only related to HashPartitioning. This change normalizes all output partitioning based on aliasing happening in Project. To remove unneeded exchanges. No New UT added. On TPCDS 1000 scale, this change improves the performance of query 95 from 330 seconds to 170 seconds by removing the extra Exchange. Closes #30300 from prakharjain09/SPARK-33399-outputpartitioning. Authored-by: Prakhar Jain <[email protected]> Signed-off-by: Takeshi Yamamuro <[email protected]> * [CARMEL-6306] Fix ut * [CARMEL-6306] Fix alias not compatible with ebay skew implementation Co-authored-by: Terry Kim <[email protected]> Co-authored-by: Prakhar Jain <[email protected]>
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Fixes test failures introduced by #1316.
For both the regression and classification cases,
val stats is the InformationGainStats for the best tree split.
stats.predict is the predicted value for the data, before the split is made.
Since 600 of the 1,000 values generated by DecisionTreeSuite.generateCategoricalDataPoints() are 1.0 and the rest 0.0, the regression tree and classification tree both correctly predict a value of 0.6 for this data now, and the assertions have been changed to reflect that.