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Posted to reviews@spark.apache.org by "WeichenXu123 (via GitHub)" <gi...@apache.org> on 2023/03/20 00:58:35 UTC
[GitHub] [spark] WeichenXu123 opened a new pull request, #40479: [CONNECT][ML][WIP] Spark connect ml scala 1
WeichenXu123 opened a new pull request, #40479:
URL: https://github.com/apache/spark/pull/40479
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[GitHub] [spark] WeichenXu123 commented on a diff in pull request #40479: [CONNECT][ML][WIP] Spark connect ML for scala client
Posted by "WeichenXu123 (via GitHub)" <gi...@apache.org>.
WeichenXu123 commented on code in PR #40479:
URL: https://github.com/apache/spark/pull/40479#discussion_r1141524125
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/ml/ConnectUtils.scala:
##########
@@ -0,0 +1,208 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml
+
+import org.apache.spark.connect.proto
+import org.apache.spark.ml.linalg.{Matrix, Vector, Matrices, Vectors}
+import org.apache.spark.ml.param.Params
+import org.apache.spark.sql.catalyst.util.{DateTimeUtils, IntervalUtils}
+import org.apache.spark.sql.connect.common.{InvalidPlanInput, LiteralValueProtoConverter}
+import org.apache.spark.sql.types.Decimal
+import org.apache.spark.unsafe.types.CalendarInterval
+
+import scala.collection.mutable
+import scala.reflect.ClassTag
+
+object ConnectUtils {
+
+ def getInstanceParamsProto(instance: Params): proto.MlParams = {
+ val builder = proto.MlParams.newBuilder()
+
+ for (param <- instance.params) {
+ instance.get(param).map { value =>
+ builder.putParams(
+ param.name,
+ LiteralValueProtoConverter.toLiteralProto(value)
+ )
+ }
+ instance.getDefault(param).map { value =>
+ builder.putParams(
+ param.name,
+ LiteralValueProtoConverter.toLiteralProto(value)
+ )
+ }
+ }
+ builder.build()
+ }
+
+ def serializeResponseValue(data: Any): proto.MlCommandResponse = {
+ data match {
+ case v: Vector => serializeVector(v)
+ case v: Matrix => serializeMatrix(v)
+ case _: Byte | _: Short | _: Int | _: Long | _: Float | _: Double | _: Boolean | _: String |
+ _: Array[_] =>
+ proto.MlCommandResponse
+ .newBuilder()
+ .setLiteral(LiteralValueProtoConverter.toLiteralProto(data))
+ .build()
+ case _ =>
+ throw new IllegalArgumentException()
+ }
+ }
+
+ def serializeVector(data: Vector): proto.MlCommandResponse = {
+ // TODO: Support sparse
+ val values = data.toArray
+ val denseBuilder = proto.Vector.Dense.newBuilder()
+ for (i <- 0 until values.length) {
+ denseBuilder.addValue(values(i))
+ }
+
+ proto.MlCommandResponse
+ .newBuilder()
+ .setVector(proto.Vector.newBuilder().setDense(denseBuilder))
+ .build()
+ }
+
+ def deserializeVector(protoValue: proto.Vector): Vector = {
+ // TODO: Support sparse
+ Vectors.dense(
+ protoValue.getDense.getValueList.stream().mapToDouble(_.doubleValue()).toArray
+ )
+ }
+
+ def deserializeMatrix(protoValue: proto.Matrix): Matrix = {
+ // TODO: Support sparse
+ val denseProto = protoValue.getDense
+ Matrices.dense(
+ denseProto.getNumRows,
+ denseProto.getNumCols,
+ denseProto.getValueList.stream().mapToDouble(_.doubleValue()).toArray
+ )
+ }
+
+ def serializeMatrix(data: Matrix): proto.MlCommandResponse = {
+ // TODO: Support sparse
+ // TODO: optimize transposed case
+ val denseBuilder = proto.Matrix.Dense.newBuilder()
+ val values = data.toArray
+ for (i <- 0 until values.length) {
+ denseBuilder.addValue(values(i))
+ }
+ denseBuilder.setNumCols(data.numCols)
+ denseBuilder.setNumRows(data.numRows)
+ denseBuilder.setIsTransposed(false)
+ proto.MlCommandResponse
+ .newBuilder()
+ .setMatrix(proto.Matrix.newBuilder().setDense(denseBuilder))
+ .build()
+ }
+
+ def deserializeResponseValue(protoValue: proto.MlCommandResponse): Any = {
+ protoValue.getMlCommandResponseTypeCase match {
+ case proto.MlCommandResponse.MlCommandResponseTypeCase.LITERAL =>
+ deserializeLiteral(protoValue.getLiteral)
+ case proto.MlCommandResponse.MlCommandResponseTypeCase.VECTOR =>
+ deserializeVector(protoValue.getVector)
+ case proto.MlCommandResponse.MlCommandResponseTypeCase.MATRIX =>
+ deserializeMatrix(protoValue.getMatrix)
+ case proto.MlCommandResponse.MlCommandResponseTypeCase.MODEL_REF =>
+ ModelRef.fromProto(protoValue.getModelRef)
+ case _ =>
+ throw new IllegalArgumentException()
+ }
+ }
+
+ def deserializeLiteral(protoValue: proto.Expression.Literal): Any = {
Review Comment:
@zhengruifeng Could you help move this utility function to "common" project ?
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #40479: [CONNECT][ML][WIP] Spark connect ML for scala client
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #40479:
URL: https://github.com/apache/spark/pull/40479#discussion_r1141950975
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala:
##########
@@ -123,4 +92,25 @@ abstract class ProbabilisticClassificationModel[
// then invoke the 'predictProbability' method of the remote model
throw new NotImplementedError
}
+
+ /**
+ *If the probability and prediction columns are set, this method returns the current model,
+ * otherwise it generates new columns for them and sets them as columns on a new copy of
+ * the current model
+ */
+ override private[classification] def findSummaryModel():
Review Comment:
I think we don't needs any private helper functions like this?
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[GitHub] [spark] github-actions[bot] commented on pull request #40479: [CONNECT][ML][WIP] Spark connect ML for scala client
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #40479:
URL: https://github.com/apache/spark/pull/40479#issuecomment-1612267661
We're closing this PR because it hasn't been updated in a while. This isn't a judgement on the merit of the PR in any way. It's just a way of keeping the PR queue manageable.
If you'd like to revive this PR, please reopen it and ask a committer to remove the Stale tag!
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #40479: [CONNECT][ML][WIP] Spark connect ML for scala client
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #40479:
URL: https://github.com/apache/spark/pull/40479#discussion_r1141916289
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/ml/ConnectUtils.scala:
##########
@@ -0,0 +1,208 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml
+
+import org.apache.spark.connect.proto
+import org.apache.spark.ml.linalg.{Matrix, Vector, Matrices, Vectors}
+import org.apache.spark.ml.param.Params
+import org.apache.spark.sql.catalyst.util.{DateTimeUtils, IntervalUtils}
+import org.apache.spark.sql.connect.common.{InvalidPlanInput, LiteralValueProtoConverter}
+import org.apache.spark.sql.types.Decimal
+import org.apache.spark.unsafe.types.CalendarInterval
+
+import scala.collection.mutable
+import scala.reflect.ClassTag
+
+object ConnectUtils {
+
+ def getInstanceParamsProto(instance: Params): proto.MlParams = {
+ val builder = proto.MlParams.newBuilder()
+
+ for (param <- instance.params) {
+ instance.get(param).map { value =>
+ builder.putParams(
+ param.name,
+ LiteralValueProtoConverter.toLiteralProto(value)
+ )
+ }
+ instance.getDefault(param).map { value =>
+ builder.putParams(
+ param.name,
+ LiteralValueProtoConverter.toLiteralProto(value)
+ )
+ }
+ }
+ builder.build()
+ }
+
+ def serializeResponseValue(data: Any): proto.MlCommandResponse = {
+ data match {
+ case v: Vector => serializeVector(v)
+ case v: Matrix => serializeMatrix(v)
+ case _: Byte | _: Short | _: Int | _: Long | _: Float | _: Double | _: Boolean | _: String |
+ _: Array[_] =>
+ proto.MlCommandResponse
+ .newBuilder()
+ .setLiteral(LiteralValueProtoConverter.toLiteralProto(data))
+ .build()
+ case _ =>
+ throw new IllegalArgumentException()
+ }
+ }
+
+ def serializeVector(data: Vector): proto.MlCommandResponse = {
+ // TODO: Support sparse
+ val values = data.toArray
+ val denseBuilder = proto.Vector.Dense.newBuilder()
+ for (i <- 0 until values.length) {
+ denseBuilder.addValue(values(i))
+ }
+
+ proto.MlCommandResponse
+ .newBuilder()
+ .setVector(proto.Vector.newBuilder().setDense(denseBuilder))
+ .build()
+ }
+
+ def deserializeVector(protoValue: proto.Vector): Vector = {
+ // TODO: Support sparse
+ Vectors.dense(
+ protoValue.getDense.getValueList.stream().mapToDouble(_.doubleValue()).toArray
+ )
+ }
+
+ def deserializeMatrix(protoValue: proto.Matrix): Matrix = {
+ // TODO: Support sparse
+ val denseProto = protoValue.getDense
+ Matrices.dense(
+ denseProto.getNumRows,
+ denseProto.getNumCols,
+ denseProto.getValueList.stream().mapToDouble(_.doubleValue()).toArray
+ )
+ }
+
+ def serializeMatrix(data: Matrix): proto.MlCommandResponse = {
+ // TODO: Support sparse
+ // TODO: optimize transposed case
+ val denseBuilder = proto.Matrix.Dense.newBuilder()
+ val values = data.toArray
+ for (i <- 0 until values.length) {
+ denseBuilder.addValue(values(i))
+ }
+ denseBuilder.setNumCols(data.numCols)
+ denseBuilder.setNumRows(data.numRows)
+ denseBuilder.setIsTransposed(false)
+ proto.MlCommandResponse
+ .newBuilder()
+ .setMatrix(proto.Matrix.newBuilder().setDense(denseBuilder))
+ .build()
+ }
+
+ def deserializeResponseValue(protoValue: proto.MlCommandResponse): Any = {
+ protoValue.getMlCommandResponseTypeCase match {
+ case proto.MlCommandResponse.MlCommandResponseTypeCase.LITERAL =>
+ deserializeLiteral(protoValue.getLiteral)
+ case proto.MlCommandResponse.MlCommandResponseTypeCase.VECTOR =>
+ deserializeVector(protoValue.getVector)
+ case proto.MlCommandResponse.MlCommandResponseTypeCase.MATRIX =>
+ deserializeMatrix(protoValue.getMatrix)
+ case proto.MlCommandResponse.MlCommandResponseTypeCase.MODEL_REF =>
+ ModelRef.fromProto(protoValue.getModelRef)
+ case _ =>
+ throw new IllegalArgumentException()
+ }
+ }
+
+ def deserializeLiteral(protoValue: proto.Expression.Literal): Any = {
Review Comment:
https://github.com/apache/spark/pull/40485
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[GitHub] [spark] WeichenXu123 commented on a diff in pull request #40479: [CONNECT][ML][WIP] Spark connect ML for scala client
Posted by "WeichenXu123 (via GitHub)" <gi...@apache.org>.
WeichenXu123 commented on code in PR #40479:
URL: https://github.com/apache/spark/pull/40479#discussion_r1142163720
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala:
##########
@@ -123,4 +92,25 @@ abstract class ProbabilisticClassificationModel[
// then invoke the 'predictProbability' method of the remote model
throw new NotImplementedError
}
+
+ /**
+ *If the probability and prediction columns are set, this method returns the current model,
+ * otherwise it generates new columns for them and sets them as columns on a new copy of
+ * the current model
+ */
+ override private[classification] def findSummaryModel():
Review Comment:
I don't think so, it is used here:
```
def evaluate(dataset: Dataset[_]): LogisticRegressionSummary = {
// Handle possible missing or invalid prediction columns
val (summaryModel, probabilityColName, predictionColName) = findSummaryModel()
...
```
and this part code we should run it in client side.
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #40479: [CONNECT][ML][WIP] Spark connect ML for scala client
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #40479:
URL: https://github.com/apache/spark/pull/40479#discussion_r1141947414
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/ml/Pipeline.scala:
##########
@@ -17,47 +17,13 @@
package org.apache.spark.ml
-import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.internal.Logging
import org.apache.spark.ml.param.{ParamMap, Params}
-import org.apache.spark.sql.types.StructType
/**
* A stage in a pipeline, either an [[Estimator]] or a [[Transformer]].
*/
abstract class PipelineStage extends Params with Logging {
- /**
- * Check transform validity and derive the output schema from the input schema.
- *
- * We check validity for interactions between parameters during `transformSchema` and raise an
- * exception if any parameter value is invalid. Parameter value checks which do not depend on
- * other parameters are handled by `Param.validate()`.
- *
- * Typical implementation should first conduct verification on schema change and parameter
- * validity, including complex parameter interaction checks.
- */
- def transformSchema(schema: StructType): StructType
Review Comment:
why removing the `transformSchema` method?
it seems in SCSC, it can be implemented with functions in `mllib-common`
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[GitHub] [spark] WeichenXu123 commented on a diff in pull request #40479: [CONNECT][ML][WIP] Spark connect ML for scala client
Posted by "WeichenXu123 (via GitHub)" <gi...@apache.org>.
WeichenXu123 commented on code in PR #40479:
URL: https://github.com/apache/spark/pull/40479#discussion_r1142159747
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/ml/Pipeline.scala:
##########
@@ -17,47 +17,13 @@
package org.apache.spark.ml
-import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.internal.Logging
import org.apache.spark.ml.param.{ParamMap, Params}
-import org.apache.spark.sql.types.StructType
/**
* A stage in a pipeline, either an [[Estimator]] or a [[Transformer]].
*/
abstract class PipelineStage extends Params with Logging {
- /**
- * Check transform validity and derive the output schema from the input schema.
- *
- * We check validity for interactions between parameters during `transformSchema` and raise an
- * exception if any parameter value is invalid. Parameter value checks which do not depend on
- * other parameters are handled by `Param.validate()`.
- *
- * Typical implementation should first conduct verification on schema change and parameter
- * validity, including complex parameter interaction checks.
- */
- def transformSchema(schema: StructType): StructType
Review Comment:
I think we should run `transformSchema` in server side, we don't need to run it in client side.
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #40479: [CONNECT][ML][WIP] Spark connect ML for scala client
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #40479:
URL: https://github.com/apache/spark/pull/40479#discussion_r1141810112
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/ml/ConnectUtils.scala:
##########
@@ -0,0 +1,208 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml
+
+import org.apache.spark.connect.proto
+import org.apache.spark.ml.linalg.{Matrix, Vector, Matrices, Vectors}
+import org.apache.spark.ml.param.Params
+import org.apache.spark.sql.catalyst.util.{DateTimeUtils, IntervalUtils}
+import org.apache.spark.sql.connect.common.{InvalidPlanInput, LiteralValueProtoConverter}
+import org.apache.spark.sql.types.Decimal
+import org.apache.spark.unsafe.types.CalendarInterval
+
+import scala.collection.mutable
+import scala.reflect.ClassTag
+
+object ConnectUtils {
+
+ def getInstanceParamsProto(instance: Params): proto.MlParams = {
+ val builder = proto.MlParams.newBuilder()
+
+ for (param <- instance.params) {
+ instance.get(param).map { value =>
+ builder.putParams(
+ param.name,
+ LiteralValueProtoConverter.toLiteralProto(value)
+ )
+ }
+ instance.getDefault(param).map { value =>
+ builder.putParams(
+ param.name,
+ LiteralValueProtoConverter.toLiteralProto(value)
+ )
+ }
+ }
+ builder.build()
+ }
+
+ def serializeResponseValue(data: Any): proto.MlCommandResponse = {
+ data match {
+ case v: Vector => serializeVector(v)
+ case v: Matrix => serializeMatrix(v)
+ case _: Byte | _: Short | _: Int | _: Long | _: Float | _: Double | _: Boolean | _: String |
+ _: Array[_] =>
+ proto.MlCommandResponse
+ .newBuilder()
+ .setLiteral(LiteralValueProtoConverter.toLiteralProto(data))
+ .build()
+ case _ =>
+ throw new IllegalArgumentException()
+ }
+ }
+
+ def serializeVector(data: Vector): proto.MlCommandResponse = {
+ // TODO: Support sparse
+ val values = data.toArray
+ val denseBuilder = proto.Vector.Dense.newBuilder()
+ for (i <- 0 until values.length) {
+ denseBuilder.addValue(values(i))
+ }
+
+ proto.MlCommandResponse
+ .newBuilder()
+ .setVector(proto.Vector.newBuilder().setDense(denseBuilder))
+ .build()
+ }
+
+ def deserializeVector(protoValue: proto.Vector): Vector = {
+ // TODO: Support sparse
+ Vectors.dense(
+ protoValue.getDense.getValueList.stream().mapToDouble(_.doubleValue()).toArray
+ )
+ }
+
+ def deserializeMatrix(protoValue: proto.Matrix): Matrix = {
+ // TODO: Support sparse
+ val denseProto = protoValue.getDense
+ Matrices.dense(
+ denseProto.getNumRows,
+ denseProto.getNumCols,
+ denseProto.getValueList.stream().mapToDouble(_.doubleValue()).toArray
+ )
+ }
+
+ def serializeMatrix(data: Matrix): proto.MlCommandResponse = {
+ // TODO: Support sparse
+ // TODO: optimize transposed case
+ val denseBuilder = proto.Matrix.Dense.newBuilder()
+ val values = data.toArray
+ for (i <- 0 until values.length) {
+ denseBuilder.addValue(values(i))
+ }
+ denseBuilder.setNumCols(data.numCols)
+ denseBuilder.setNumRows(data.numRows)
+ denseBuilder.setIsTransposed(false)
+ proto.MlCommandResponse
+ .newBuilder()
+ .setMatrix(proto.Matrix.newBuilder().setDense(denseBuilder))
+ .build()
+ }
+
+ def deserializeResponseValue(protoValue: proto.MlCommandResponse): Any = {
+ protoValue.getMlCommandResponseTypeCase match {
+ case proto.MlCommandResponse.MlCommandResponseTypeCase.LITERAL =>
+ deserializeLiteral(protoValue.getLiteral)
+ case proto.MlCommandResponse.MlCommandResponseTypeCase.VECTOR =>
+ deserializeVector(protoValue.getVector)
+ case proto.MlCommandResponse.MlCommandResponseTypeCase.MATRIX =>
+ deserializeMatrix(protoValue.getMatrix)
+ case proto.MlCommandResponse.MlCommandResponseTypeCase.MODEL_REF =>
+ ModelRef.fromProto(protoValue.getModelRef)
+ case _ =>
+ throw new IllegalArgumentException()
+ }
+ }
+
+ def deserializeLiteral(protoValue: proto.Expression.Literal): Any = {
Review Comment:
a util function convert `proto.Expression.Literal` to `Any` value?
let me take a look
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[GitHub] [spark] WeichenXu123 commented on a diff in pull request #40479: [CONNECT][ML][WIP] Spark connect ML for scala client
Posted by "WeichenXu123 (via GitHub)" <gi...@apache.org>.
WeichenXu123 commented on code in PR #40479:
URL: https://github.com/apache/spark/pull/40479#discussion_r1142784162
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/ml/Pipeline.scala:
##########
@@ -17,47 +17,13 @@
package org.apache.spark.ml
-import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.internal.Logging
import org.apache.spark.ml.param.{ParamMap, Params}
-import org.apache.spark.sql.types.StructType
/**
* A stage in a pipeline, either an [[Estimator]] or a [[Transformer]].
*/
abstract class PipelineStage extends Params with Logging {
- /**
- * Check transform validity and derive the output schema from the input schema.
- *
- * We check validity for interactions between parameters during `transformSchema` and raise an
- * exception if any parameter value is invalid. Parameter value checks which do not depend on
- * other parameters are handled by `Param.validate()`.
- *
- * Typical implementation should first conduct verification on schema change and parameter
- * validity, including complex parameter interaction checks.
- */
- def transformSchema(schema: StructType): StructType
Review Comment:
Similarly, in pyspark side, the `Transformer/ JavaTransformer` also does not do any schema transformation.
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[GitHub] [spark] github-actions[bot] closed pull request #40479: [CONNECT][ML][WIP] Spark connect ML for scala client
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] closed pull request #40479: [CONNECT][ML][WIP] Spark connect ML for scala client
URL: https://github.com/apache/spark/pull/40479
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