[jira] [Updated] (SPARK-9120) Add multivariate regression (or prediction) interface
[ https://issues.apache.org/jira/browse/SPARK-9120?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Joseph K. Bradley updated SPARK-9120: - Shepherd: (was: Joseph K. Bradley) > Add multivariate regression (or prediction) interface > - > > Key: SPARK-9120 > URL: https://issues.apache.org/jira/browse/SPARK-9120 > Project: Spark > Issue Type: Improvement > Components: ML >Affects Versions: 1.4.0 >Reporter: Alexander Ulanov >Priority: Major > Original Estimate: 1h > Remaining Estimate: 1h > > org.apache.spark.ml.regression.RegressionModel supports prediction only for a > single variable with a method "predict:Double" by extending the Predictor. > There is a need for multivariate prediction, at least for regression. I > propose to modify "RegressionModel" interface similarly to how it is done in > "ClassificationModel", which supports multiclass classification. It has > "predict:Double" and "predictRaw:Vector". Analogously, "RegressionModel" > should have something like "predictMultivariate:Vector". > Update: After reading the design docs, adding "predictMultivariate" to > RegressionModel does not seem reasonable to me anymore. The issue is as > follows. RegressionModel has "predict:Double". Its "train" method uses > "predict:Double" for prediction, i.e. PredictionModel (and RegressionModel) > is hard-coded to have only one output. There exist a similar problem in MLLib > (https://issues.apache.org/jira/browse/SPARK-5362). > The possible solution for this problem might require to redesign the class > hierarchy or addition of a separate interface that extends model. Though the > latter means code duplication. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-9120) Add multivariate regression (or prediction) interface
[ https://issues.apache.org/jira/browse/SPARK-9120?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Alexander Ulanov updated SPARK-9120: Description: org.apache.spark.ml.regression.RegressionModel supports prediction only for a single variable with a method "predict:Double" by extending the Predictor. There is a need for multivariate prediction, at least for regression. I propose to modify "RegressionModel" interface similarly to how it is done in "ClassificationModel", which supports multiclass classification. It has "predict:Double" and "predictRaw:Vector". Analogously, "RegressionModel" should have something like "predictMultivariate:Vector". Update: After reading the design docs, adding "predictMultivariate" to RegressionModel does not seem reasonable to me anymore. The issue is as follows. RegressionModel has "predict:Double". Its "train" method uses "predict:Double" for prediction, i.e. PredictionModel (and RegressionModel) is hard-coded to have only one output. There exist a similar problem in MLLib (https://issues.apache.org/jira/browse/SPARK-5362). The possible solution for this problem might require to redesign the class hierarchy or addition of a separate interface that extends model. Though the latter means code duplication. was: org.apache.spark.ml.regression.RegressionModel supports prediction only for a single variable with a method "predict:Double" by extending the Predictor. There is a need for multivariate prediction, at least for regression. I propose to modify "RegressionModel" interface similarly to how it is done in "ClassificationModel", which supports multiclass classification. It has "predict:Double" and "predictRaw:Vector". Analogously, "RegressionModel" should have something like "predictMultivariate:Vector". Update: After reading the design docs, adding "predictMultivariate" to RegressionModel does not seem reasonable to me anymore. The issue is as follows. RegressionModel extends PredictionModel which has "predict:Double". Its "train" method uses "predict:Double" for prediction, i.e. PredictionModel (and RegressionModel) is hard-coded to have only one output. There exist a similar problem in MLLib (https://issues.apache.org/jira/browse/SPARK-5362). The possible solution for this problem might require to redesign the class hierarchy or addition of a separate interface that extends model. Though the latter means code duplication. > Add multivariate regression (or prediction) interface > - > > Key: SPARK-9120 > URL: https://issues.apache.org/jira/browse/SPARK-9120 > Project: Spark > Issue Type: Improvement > Components: ML >Affects Versions: 1.4.0 >Reporter: Alexander Ulanov > Original Estimate: 1h > Remaining Estimate: 1h > > org.apache.spark.ml.regression.RegressionModel supports prediction only for a > single variable with a method "predict:Double" by extending the Predictor. > There is a need for multivariate prediction, at least for regression. I > propose to modify "RegressionModel" interface similarly to how it is done in > "ClassificationModel", which supports multiclass classification. It has > "predict:Double" and "predictRaw:Vector". Analogously, "RegressionModel" > should have something like "predictMultivariate:Vector". > Update: After reading the design docs, adding "predictMultivariate" to > RegressionModel does not seem reasonable to me anymore. The issue is as > follows. RegressionModel has "predict:Double". Its "train" method uses > "predict:Double" for prediction, i.e. PredictionModel (and RegressionModel) > is hard-coded to have only one output. There exist a similar problem in MLLib > (https://issues.apache.org/jira/browse/SPARK-5362). > The possible solution for this problem might require to redesign the class > hierarchy or addition of a separate interface that extends model. Though the > latter means code duplication. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-9120) Add multivariate regression (or prediction) interface
[ https://issues.apache.org/jira/browse/SPARK-9120?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Sean Owen updated SPARK-9120: - Fix Version/s: (was: 1.4.0) > Add multivariate regression (or prediction) interface > - > > Key: SPARK-9120 > URL: https://issues.apache.org/jira/browse/SPARK-9120 > Project: Spark > Issue Type: Improvement > Components: ML >Affects Versions: 1.4.0 >Reporter: Alexander Ulanov > Original Estimate: 1h > Remaining Estimate: 1h > > org.apache.spark.ml.regression.RegressionModel supports prediction only for a > single variable with a method "predict:Double" by extending the Predictor. > There is a need for multivariate prediction, at least for regression. I > propose to modify "RegressionModel" interface similarly to how it is done in > "ClassificationModel", which supports multiclass classification. It has > "predict:Double" and "predictRaw:Vector". Analogously, "RegressionModel" > should have something like "predictMultivariate:Vector". > Update: After reading the design docs, adding "predictMultivariate" to > RegressionModel does not seem reasonable to me anymore. The issue is as > follows. RegressionModel extends PredictionModel which has "predict:Double". > Its "train" method uses "predict:Double" for prediction, i.e. PredictionModel > (and RegressionModel) is hard-coded to have only one output. There exist a > similar problem in MLLib (https://issues.apache.org/jira/browse/SPARK-5362). > The possible solution for this problem might require to redesign the class > hierarchy or addition of a separate interface that extends model. Though the > latter means code duplication. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-9120) Add multivariate regression (or prediction) interface
[ https://issues.apache.org/jira/browse/SPARK-9120?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Joseph K. Bradley updated SPARK-9120: - Target Version/s: (was: 1.5.0) Add multivariate regression (or prediction) interface - Key: SPARK-9120 URL: https://issues.apache.org/jira/browse/SPARK-9120 Project: Spark Issue Type: Improvement Components: ML Affects Versions: 1.4.0 Reporter: Alexander Ulanov Fix For: 1.4.0 Original Estimate: 1h Remaining Estimate: 1h org.apache.spark.ml.regression.RegressionModel supports prediction only for a single variable with a method predict:Double by extending the Predictor. There is a need for multivariate prediction, at least for regression. I propose to modify RegressionModel interface similarly to how it is done in ClassificationModel, which supports multiclass classification. It has predict:Double and predictRaw:Vector. Analogously, RegressionModel should have something like predictMultivariate:Vector. Update: After reading the design docs, adding predictMultivariate to RegressionModel does not seem reasonable to me anymore. The issue is as follows. RegressionModel extends PredictionModel which has predict:Double. Its train method uses predict:Double for prediction, i.e. PredictionModel (and RegressionModel) is hard-coded to have only one output. There exist a similar problem in MLLib (https://issues.apache.org/jira/browse/SPARK-5362). The possible solution for this problem might require to redesign the class hierarchy or addition of a separate interface that extends model. Though the latter means code duplication. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-9120) Add multivariate regression (or prediction) interface
[ https://issues.apache.org/jira/browse/SPARK-9120?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Alexander Ulanov updated SPARK-9120: Description: org.apache.spark.ml.regression.RegressionModel supports prediction only for a single variable with a method predict:Double by extending the Predictor. There is a need for multivariate prediction, at least for regression. I propose to modify RegressionModel interface similarly to how it is done in ClassificationModel, which supports multiclass classification. It has predict:Double and predictRaw:Vector. Analogously, RegressionModel should have something like predictMultivariate:Vector. Update:After reading the design docs, adding predictMultivariate to RegressionModel does not seem reasonable to me anymore. The issue is as follows. RegressionModel extends PredictionModel which has predict:Double. Its train method uses predict:Double for prediction, i.e. PredictionModel is hard-coded to have only one output. It is the same problem that I pointed out long time ago in MLLib ( was: org.apache.spark.ml.regression.RegressionModel supports prediction only for a single variable with a method predict:Double by extending the Predictor. There is a need for multivariate prediction, at least for regression. I propose to modify RegressionModel interface similarly to how it is done in ClassificationModel, which supports multiclass classification. It has predict:Double and predictRaw:Vector. Analogously, RegressionModel should have something like predictMultivariate:Vector. Add multivariate regression (or prediction) interface - Key: SPARK-9120 URL: https://issues.apache.org/jira/browse/SPARK-9120 Project: Spark Issue Type: Improvement Components: ML Affects Versions: 1.4.0 Reporter: Alexander Ulanov Fix For: 1.4.0 Original Estimate: 1h Remaining Estimate: 1h org.apache.spark.ml.regression.RegressionModel supports prediction only for a single variable with a method predict:Double by extending the Predictor. There is a need for multivariate prediction, at least for regression. I propose to modify RegressionModel interface similarly to how it is done in ClassificationModel, which supports multiclass classification. It has predict:Double and predictRaw:Vector. Analogously, RegressionModel should have something like predictMultivariate:Vector. Update:After reading the design docs, adding predictMultivariate to RegressionModel does not seem reasonable to me anymore. The issue is as follows. RegressionModel extends PredictionModel which has predict:Double. Its train method uses predict:Double for prediction, i.e. PredictionModel is hard-coded to have only one output. It is the same problem that I pointed out long time ago in MLLib ( -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-9120) Add multivariate regression (or prediction) interface
[ https://issues.apache.org/jira/browse/SPARK-9120?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Alexander Ulanov updated SPARK-9120: Description: org.apache.spark.ml.regression.RegressionModel supports prediction only for a single variable with a method predict:Double by extending the Predictor. There is a need for multivariate prediction, at least for regression. I propose to modify RegressionModel interface similarly to how it is done in ClassificationModel, which supports multiclass classification. It has predict:Double and predictRaw:Vector. Analogously, RegressionModel should have something like predictMultivariate:Vector. Update: After reading the design docs, adding predictMultivariate to RegressionModel does not seem reasonable to me anymore. The issue is as follows. RegressionModel extends PredictionModel which has predict:Double. Its train method uses predict:Double for prediction, i.e. PredictionModel (and RegressionModel) is hard-coded to have only one output. There exist a similar problem in MLLib (https://issues.apache.org/jira/browse/SPARK-5362). The possible solution for this problem might require to redesign the class hierarchy or addition of a separate interface that extends model. Though the latter means code duplication. was: org.apache.spark.ml.regression.RegressionModel supports prediction only for a single variable with a method predict:Double by extending the Predictor. There is a need for multivariate prediction, at least for regression. I propose to modify RegressionModel interface similarly to how it is done in ClassificationModel, which supports multiclass classification. It has predict:Double and predictRaw:Vector. Analogously, RegressionModel should have something like predictMultivariate:Vector. Update:After reading the design docs, adding predictMultivariate to RegressionModel does not seem reasonable to me anymore. The issue is as follows. RegressionModel extends PredictionModel which has predict:Double. Its train method uses predict:Double for prediction, i.e. PredictionModel is hard-coded to have only one output. It is the same problem that I pointed out long time ago in MLLib ( Add multivariate regression (or prediction) interface - Key: SPARK-9120 URL: https://issues.apache.org/jira/browse/SPARK-9120 Project: Spark Issue Type: Improvement Components: ML Affects Versions: 1.4.0 Reporter: Alexander Ulanov Fix For: 1.4.0 Original Estimate: 1h Remaining Estimate: 1h org.apache.spark.ml.regression.RegressionModel supports prediction only for a single variable with a method predict:Double by extending the Predictor. There is a need for multivariate prediction, at least for regression. I propose to modify RegressionModel interface similarly to how it is done in ClassificationModel, which supports multiclass classification. It has predict:Double and predictRaw:Vector. Analogously, RegressionModel should have something like predictMultivariate:Vector. Update: After reading the design docs, adding predictMultivariate to RegressionModel does not seem reasonable to me anymore. The issue is as follows. RegressionModel extends PredictionModel which has predict:Double. Its train method uses predict:Double for prediction, i.e. PredictionModel (and RegressionModel) is hard-coded to have only one output. There exist a similar problem in MLLib (https://issues.apache.org/jira/browse/SPARK-5362). The possible solution for this problem might require to redesign the class hierarchy or addition of a separate interface that extends model. Though the latter means code duplication. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
