[jira] [Created] (IGNITE-11772) [ML] Broken javadoc
Yury Babak created IGNITE-11772: --- Summary: [ML] Broken javadoc Key: IGNITE-11772 URL: https://issues.apache.org/jira/browse/IGNITE-11772 Project: Ignite Issue Type: Bug Components: ml Reporter: Yury Babak Assignee: Yury Babak Fix For: 2.7.5 [08:56:09][WARNING] Javadoc Warnings [08:56:09][WARNING] /opt/buildagent/work/5688f4d35e09c9c2/modules/ml/src/main/java/org/apache/ignite/ml/composition/boosting/convergence/simple/ConvergenceCheckerStub.java:51: warning - @param argument "vectorizer" is not a parameter name. [08:56:09][WARNING] /opt/buildagent/work/5688f4d35e09c9c2/modules/ml/src/main/java/org/apache/ignite/ml/structures/DatasetRow.java:119: warning - @return tag cannot be used in method with void return type. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-11759) [ML] Duplicate depenpecies for ml artifacts
Yury Babak created IGNITE-11759: --- Summary: [ML] Duplicate depenpecies for ml artifacts Key: IGNITE-11759 URL: https://issues.apache.org/jira/browse/IGNITE-11759 Project: Ignite Issue Type: Improvement Components: ml Affects Versions: 2.7 Reporter: Yury Babak -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-11449) [ML] API for Feature/Label extracting
Yury Babak created IGNITE-11449: --- Summary: [ML] API for Feature/Label extracting Key: IGNITE-11449 URL: https://issues.apache.org/jira/browse/IGNITE-11449 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Assignee: Alexey Platonov Replace current lambdas with fixed API -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-11261) [ML] Flaky test(testNaiveBaggingLogRegression)
Yury Babak created IGNITE-11261: --- Summary: [ML] Flaky test(testNaiveBaggingLogRegression) Key: IGNITE-11261 URL: https://issues.apache.org/jira/browse/IGNITE-11261 Project: Ignite Issue Type: Bug Components: ml Reporter: Yury Babak Assignee: Artem Malykh -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-11145) [ML] Add vector projection
Yury Babak created IGNITE-11145: --- Summary: [ML] Add vector projection Key: IGNITE-11145 URL: https://issues.apache.org/jira/browse/IGNITE-11145 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Assignee: Yury Babak Fix For: 2.8 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-11144) [ML] Create example for FeatureLabelExtractor
Yury Babak created IGNITE-11144: --- Summary: [ML] Create example for FeatureLabelExtractor Key: IGNITE-11144 URL: https://issues.apache.org/jira/browse/IGNITE-11144 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Assignee: Artem Malykh Fix For: 2.8 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-11138) [ML] Predict from SQL
Yury Babak created IGNITE-11138: --- Summary: [ML] Predict from SQL Key: IGNITE-11138 URL: https://issues.apache.org/jira/browse/IGNITE-11138 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Assignee: Anton Dmitriev Fix For: 2.8 We want to have an implementation for model predict for SQL queries -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-11137) [ML] IgniteModelStorage
Yury Babak created IGNITE-11137: --- Summary: [ML] IgniteModelStorage Key: IGNITE-11137 URL: https://issues.apache.org/jira/browse/IGNITE-11137 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Assignee: Anton Dmitriev Fix For: 2.8 We want to wrap Model storage by IgniteModelStorage. This wrapper should: * hide all serialization/deserialization activities for models * check args and work with paths which not exist yet * cache used models from storage -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-11028) [ML] Flaky test(KeepBinaryTest)
Yury Babak created IGNITE-11028: --- Summary: [ML] Flaky test(KeepBinaryTest) Key: IGNITE-11028 URL: https://issues.apache.org/jira/browse/IGNITE-11028 Project: Ignite Issue Type: Bug Components: ml Reporter: Yury Babak Assignee: Yury Babak -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-11010) [ML] Use seed from learningEnviroment for KMeans trainer
Yury Babak created IGNITE-11010: --- Summary: [ML] Use seed from learningEnviroment for KMeans trainer Key: IGNITE-11010 URL: https://issues.apache.org/jira/browse/IGNITE-11010 Project: Ignite Issue Type: Bug Components: ml Reporter: Yury Babak Assignee: Yury Babak Fix For: 2.8 Currently, k-means use own seed for random. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10700) [ML] Working with Binary Objects
Yury Babak created IGNITE-10700: --- Summary: [ML] Working with Binary Objects Key: IGNITE-10700 URL: https://issues.apache.org/jira/browse/IGNITE-10700 Project: Ignite Issue Type: Improvement Reporter: Yury Babak Assignee: Yury Babak Fix For: 2.8 Currently, we do not support working with caches which contains Binary Objects, we should add support of building datasets from those objects. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10641) [ML] Package reorganization
Yury Babak created IGNITE-10641: --- Summary: [ML] Package reorganization Key: IGNITE-10641 URL: https://issues.apache.org/jira/browse/IGNITE-10641 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Assignee: Yury Babak Current package organization in ML module is incomprehensible and sloppy. So I want to suggest reorganize those packages -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10574) [ML] Design API for Ensemble Training
Yury Babak created IGNITE-10574: --- Summary: [ML] Design API for Ensemble Training Key: IGNITE-10574 URL: https://issues.apache.org/jira/browse/IGNITE-10574 Project: Ignite Issue Type: Sub-task Components: ml Reporter: Yury Babak Currently, we have bagging and boosting. And for boosting we have the separate trainer(GDBTrainer), but for bagging, we have the static method inside TrainerTransformers class. We should choose what approach is better for us. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10573) [ML] Consistent API for Ensemble training
Yury Babak created IGNITE-10573: --- Summary: [ML] Consistent API for Ensemble training Key: IGNITE-10573 URL: https://issues.apache.org/jira/browse/IGNITE-10573 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10575) [ML] Add examples and update tutorial for ensemble training
Yury Babak created IGNITE-10575: --- Summary: [ML] Add examples and update tutorial for ensemble training Key: IGNITE-10575 URL: https://issues.apache.org/jira/browse/IGNITE-10575 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10546) [ML] GMM with adding and removal of components
Yury Babak created IGNITE-10546: --- Summary: [ML] GMM with adding and removal of components Key: IGNITE-10546 URL: https://issues.apache.org/jira/browse/IGNITE-10546 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10548) [ML] Classificator based on GMM
Yury Babak created IGNITE-10548: --- Summary: [ML] Classificator based on GMM Key: IGNITE-10548 URL: https://issues.apache.org/jira/browse/IGNITE-10548 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10541) [ML] Umbrella: EM (GMM) with adding and removal of components
Yury Babak created IGNITE-10541: --- Summary: [ML] Umbrella: EM (GMM) with adding and removal of components Key: IGNITE-10541 URL: https://issues.apache.org/jira/browse/IGNITE-10541 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Yury Babak -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10547) [ML] Examples of GMM usage
Yury Babak created IGNITE-10547: --- Summary: [ML] Examples of GMM usage Key: IGNITE-10547 URL: https://issues.apache.org/jira/browse/IGNITE-10547 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10545) [ML] Kullback–Leibler divergence
Yury Babak created IGNITE-10545: --- Summary: [ML] Kullback–Leibler divergence Key: IGNITE-10545 URL: https://issues.apache.org/jira/browse/IGNITE-10545 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak [wiki link|https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence] -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10544) [ML] GMM with fixed components
Yury Babak created IGNITE-10544: --- Summary: [ML] GMM with fixed components Key: IGNITE-10544 URL: https://issues.apache.org/jira/browse/IGNITE-10544 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10543) [ML] Test/train sample generator
Yury Babak created IGNITE-10543: --- Summary: [ML] Test/train sample generator Key: IGNITE-10543 URL: https://issues.apache.org/jira/browse/IGNITE-10543 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10542) [ML] Distributive models with basic algebra
Yury Babak created IGNITE-10542: --- Summary: [ML] Distributive models with basic algebra Key: IGNITE-10542 URL: https://issues.apache.org/jira/browse/IGNITE-10542 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10510) [ML] Use OneVsRest for SVMLinearMultiClassClassificationTrainer
Yury Babak created IGNITE-10510: --- Summary: [ML] Use OneVsRest for SVMLinearMultiClassClassificationTrainer Key: IGNITE-10510 URL: https://issues.apache.org/jira/browse/IGNITE-10510 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Assignee: Alexey Platonov Fix For: 2.8 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10481) [ML] Examples of stacking usage
Yury Babak created IGNITE-10481: --- Summary: [ML] Examples of stacking usage Key: IGNITE-10481 URL: https://issues.apache.org/jira/browse/IGNITE-10481 Project: Ignite Issue Type: Sub-task Components: ml Reporter: Yury Babak -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10480) [ML] Stacking for training and inference
Yury Babak created IGNITE-10480: --- Summary: [ML] Stacking for training and inference Key: IGNITE-10480 URL: https://issues.apache.org/jira/browse/IGNITE-10480 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Artem Malykh Stacking is an ensemble learning technique that combines multiple classification or regression models via a meta-classifier or a meta-regressor. The base level models are trained based on a complete training set, then the meta-model is trained on the outputs of the base level model as features. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10479) [ML] Umbrella: Ensemble training and inference
Yury Babak created IGNITE-10479: --- Summary: [ML] Umbrella: Ensemble training and inference Key: IGNITE-10479 URL: https://issues.apache.org/jira/browse/IGNITE-10479 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak We want to unify API/usage of any ensembles of models. Currently we already have only boosting and bagging and we want to implement stacking. Stacking is an ensemble learning technique that combines multiple classification or regression models via a meta-classifier or a meta-regressor. The base level models are trained based on a complete training set, then the meta-model is trained on the outputs of the base level model as features. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10439) [ML] Examples of DBSCAN
Yury Babak created IGNITE-10439: --- Summary: [ML] Examples of DBSCAN Key: IGNITE-10439 URL: https://issues.apache.org/jira/browse/IGNITE-10439 Project: Ignite Issue Type: Sub-task Reporter: Yury Babak We need an example for DBSCAN usage -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10438) [ML] DBSCAN
Yury Babak created IGNITE-10438: --- Summary: [ML] DBSCAN Key: IGNITE-10438 URL: https://issues.apache.org/jira/browse/IGNITE-10438 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Density-based spatial clustering of applications with noise (DBSCAN) [wiki description|https://en.wikipedia.org/wiki/DBSCAN] We could test this algorithm on TWO_CLASSED_IRIS and IRIS (see MLSandboxDatasets enum) -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10419) [ML] Move person dataset to SandboxMLCache class
Yury Babak created IGNITE-10419: --- Summary: [ML] Move person dataset to SandboxMLCache class Key: IGNITE-10419 URL: https://issues.apache.org/jira/browse/IGNITE-10419 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Fix For: 2.8 How we have duplicated code in examples, simple cache with several Person records. We should move this cache creation code into SandboxMLCache class -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10382) [ML] Move datasets from example to ml module
Yury Babak created IGNITE-10382: --- Summary: [ML] Move datasets from example to ml module Key: IGNITE-10382 URL: https://issues.apache.org/jira/browse/IGNITE-10382 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Fix For: 2.8 We use mostly same datasets for ml tests and for ml examples. So we could move those datasets to ml module -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10289) [ML] Import models from XGBoost
Yury Babak created IGNITE-10289: --- Summary: [ML] Import models from XGBoost Key: IGNITE-10289 URL: https://issues.apache.org/jira/browse/IGNITE-10289 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Anton Dmitriev We want to have the ability of import model from 3rd part ml libraries into Apache Ignite. We could start this process from XGBoost library for trees and GDB. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10288) [ML] Model inference
Yury Babak created IGNITE-10288: --- Summary: [ML] Model inference Key: IGNITE-10288 URL: https://issues.apache.org/jira/browse/IGNITE-10288 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak We need a convenient API for model inference. The current idea is to utilize Service Grid for this purpose. We should have two options, first is deliver a model to any node(server or client) and infer this model on that node. The second approach is to pin a model to a specific server and infer model on that server, it could be useful in case if we need some specific hardware which we don't have at any server like a GPU or TPU. So the first approach is suitable for lightweight models and the second approach is suitable for some complex models like Neural Networks. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10287) [ML] Model storage
Yury Babak created IGNITE-10287: --- Summary: [ML] Model storage Key: IGNITE-10287 URL: https://issues.apache.org/jira/browse/IGNITE-10287 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak We want to have some storage for any kind of models in Apache Ignite. It should allow storing a model from any node after training and evaluation(MB with some meta info and model metrics) and provide API to get this model to any node. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10286) [ML] Umbrella: Model serving
Yury Babak created IGNITE-10286: --- Summary: [ML] Umbrella: Model serving Key: IGNITE-10286 URL: https://issues.apache.org/jira/browse/IGNITE-10286 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Yury Babak We want to have convenient API for model serving. It means that we need a mechanism for storing models and infer them inside Apache Ignite. For now, I see 2 important features - distributed storage for any models and inference. >From my point of view, we could use some built-in(predefined) cache as model >storage. And use service grid for model inference. We could implement some >"ModelService" for access to our storage, receive the list of all suitable >model(including model metrics and some other information about a model), >choose one(or several) and infer it from this service. Model from TF should also use the same mechanisms for storing and inference. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10145) [ML] ROC AUC score
Yury Babak created IGNITE-10145: --- Summary: [ML] ROC AUC score Key: IGNITE-10145 URL: https://issues.apache.org/jira/browse/IGNITE-10145 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Fix For: 2.8 Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores. We want to implement this score for our models. Some links: * [wiki|https://en.wikipedia.org/wiki/Receiver_operating_characteristic] * [google dev|https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc] -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-10026) [ML] Broken example: GaussianNaiveBayesTrainerExample
Yury Babak created IGNITE-10026: --- Summary: [ML] Broken example: GaussianNaiveBayesTrainerExample Key: IGNITE-10026 URL: https://issues.apache.org/jira/browse/IGNITE-10026 Project: Ignite Issue Type: Bug Components: ml Affects Versions: 2.8 Reporter: Yury Babak Assignee: Yury Babak Fix For: 2.8 Broken example: GaussianNaiveBayesTrainerExample -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-9685) [ML] Add ignite-tensorflow module to build artifacts
Yury Babak created IGNITE-9685: -- Summary: [ML] Add ignite-tensorflow module to build artifacts Key: IGNITE-9685 URL: https://issues.apache.org/jira/browse/IGNITE-9685 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Peter Ivanov Fix For: 2.7 We want to release Apache Ignite TensorFlow Integration Module with other Ignite tools. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-9437) [ML] Add performance benchmarks
Yury Babak created IGNITE-9437: -- Summary: [ML] Add performance benchmarks Key: IGNITE-9437 URL: https://issues.apache.org/jira/browse/IGNITE-9437 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Assignee: Oleg Ignatenko Fix For: 2.7 We want to have some performance benchmarks for ML algorithms -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-9415) [ML] Using sparce vectors in LSQR and MLP
Yury Babak created IGNITE-9415: -- Summary: [ML] Using sparce vectors in LSQR and MLP Key: IGNITE-9415 URL: https://issues.apache.org/jira/browse/IGNITE-9415 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Fix For: 2.7 We need to investigate and apply sparce vectors support in BLAS for LSQR and MLP (or implement own version) -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-9414) [ML] Using sparce vectors in Tree-based algorithms.
Yury Babak created IGNITE-9414: -- Summary: [ML] Using sparce vectors in Tree-based algorithms. Key: IGNITE-9414 URL: https://issues.apache.org/jira/browse/IGNITE-9414 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Fix For: 2.7 We need to support sparce vectors in DecisionTrees, RF, GDB -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-9413) [ML] Learning rate optimization for GDB.
Yury Babak created IGNITE-9413: -- Summary: [ML] Learning rate optimization for GDB. Key: IGNITE-9413 URL: https://issues.apache.org/jira/browse/IGNITE-9413 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Fix For: 2.7 We need to support learning rate optimization while training for MSE-loss and Log-loss -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-9412) [ML] GDB convergence by error support.
Yury Babak created IGNITE-9412: -- Summary: [ML] GDB convergence by error support. Key: IGNITE-9412 URL: https://issues.apache.org/jira/browse/IGNITE-9412 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Fix For: 2.7 We need to support early training interruption when GDB has small error rate on learning sample -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-9387) [ML] Model updating
Yury Babak created IGNITE-9387: -- Summary: [ML] Model updating Key: IGNITE-9387 URL: https://issues.apache.org/jira/browse/IGNITE-9387 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Assignee: Alexey Platonov Fix For: 2.7 In trainer interface we need to support model updating by batches after first training -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-9237) [ML] Random forest optimization
Yury Babak created IGNITE-9237: -- Summary: [ML] Random forest optimization Key: IGNITE-9237 URL: https://issues.apache.org/jira/browse/IGNITE-9237 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Assignee: Alexey Platonov Fix For: 2.7 We need to implement best split selection by statistics over impurity data and share this data for several nodes in several trees while learning process. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-9158) [ML] Pipeline
Yury Babak created IGNITE-9158: -- Summary: [ML] Pipeline Key: IGNITE-9158 URL: https://issues.apache.org/jira/browse/IGNITE-9158 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Aleksey Zinoviev Fix For: 2.7 We want to implement our own pipeline for ML operations. More details in [dev-list|http://apache-ignite-developers.2346864.n4.nabble.com/ML-Machine-Learning-Pipeline-Improvement-tt32772.html] -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-9065) Gradient boosting optimization
Yury Babak created IGNITE-9065: -- Summary: Gradient boosting optimization Key: IGNITE-9065 URL: https://issues.apache.org/jira/browse/IGNITE-9065 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Assignee: Alexey Platonov Fix For: 2.7 We need to optimize GDB learning by reusing same index for learning decision trees. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-9064) Decision tree optimization
Yury Babak created IGNITE-9064: -- Summary: Decision tree optimization Key: IGNITE-9064 URL: https://issues.apache.org/jira/browse/IGNITE-9064 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Assignee: Alexey Platonov Fix For: 2.7 We need to optimize impurity function calculation by additional index structure for all sorted features and reusing it in learning iterations. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-9034) [ML] Add Estimator API support to TensorFlow cluster on top of Apache Ignite
Yury Babak created IGNITE-9034: -- Summary: [ML] Add Estimator API support to TensorFlow cluster on top of Apache Ignite Key: IGNITE-9034 URL: https://issues.apache.org/jira/browse/IGNITE-9034 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Assignee: Anton Dmitriev Fix For: 2.7 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-9021) [ML] Refactor vectors to dence/sparse
Yury Babak created IGNITE-9021: -- Summary: [ML] Refactor vectors to dence/sparse Key: IGNITE-9021 URL: https://issues.apache.org/jira/browse/IGNITE-9021 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Assignee: Aleksey Zinoviev Fix For: 2.7 We want to remove all unused implementations of Vector interface. Same for matrices. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8981) [ML] Parallel optimizations for BaggingTrainer
Yury Babak created IGNITE-8981: -- Summary: [ML] Parallel optimizations for BaggingTrainer Key: IGNITE-8981 URL: https://issues.apache.org/jira/browse/IGNITE-8981 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Assignee: Alexey Platonov Fix For: 2.7 We could improve performance of BaggingTrainer using parallel optimizations. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8924) [ML] Parameter Grid for tuning hyper-parameters in Cross-Validation process
Yury Babak created IGNITE-8924: -- Summary: [ML] Parameter Grid for tuning hyper-parameters in Cross-Validation process Key: IGNITE-8924 URL: https://issues.apache.org/jira/browse/IGNITE-8924 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Aleksey Zinoviev Fix For: 2.7 We want to have an analogue of Parameter Grid from scikit-learn to tune hyper-parameters in Cross-Validation process. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8907) [ML] Using vectors in featureExtractor
Yury Babak created IGNITE-8907: -- Summary: [ML] Using vectors in featureExtractor Key: IGNITE-8907 URL: https://issues.apache.org/jira/browse/IGNITE-8907 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Assignee: Alexey Platonov Fix For: 2.7 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8867) Bootstrapping for learning sample
Yury Babak created IGNITE-8867: -- Summary: Bootstrapping for learning sample Key: IGNITE-8867 URL: https://issues.apache.org/jira/browse/IGNITE-8867 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Fix For: 2.7 Need to implement bootstrapping algorithm in Bagging-classifier -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8840) Random Forest
Yury Babak created IGNITE-8840: -- Summary: Random Forest Key: IGNITE-8840 URL: https://issues.apache.org/jira/browse/IGNITE-8840 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Fix For: 2.6 We want to implement random forest algorithm. It should be based on our implementation of decision trees. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8795) Add ability to start and maintain TensorFlow cluster on top of Apache Ignite
Yury Babak created IGNITE-8795: -- Summary: Add ability to start and maintain TensorFlow cluster on top of Apache Ignite Key: IGNITE-8795 URL: https://issues.apache.org/jira/browse/IGNITE-8795 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Anton Dmitriev Fix For: 2.6 As described in the design document (https://docs.google.com/document/d/1jROIahK1rc7bSgOvhJhfpMqIGvht_IE8zn5NAt6x8ks/edit?usp=sharing) Distributed TensorFlow is based on TensorFlow cluster concept. It's a set of TensorFlow processes started among the cluster and available througth the gRPC interfaces. It's assumed that these processes contain heavy operations that requires data to be stored locally on the nodes where the processes running. Apache Ignite admits the data to be moved from one node to another as result of node failure of rebalancing. As result the TensorFlow cluster should be changed dynamically as well as TensorFlow Cache (follow-the-data strategy). -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8792) Introduction of TensorFlow integration module
Yury Babak created IGNITE-8792: -- Summary: Introduction of TensorFlow integration module Key: IGNITE-8792 URL: https://issues.apache.org/jira/browse/IGNITE-8792 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Yury Babak Fix For: 2.6 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8741) [ML] Make a tutorial for data preprocessing
Yury Babak created IGNITE-8741: -- Summary: [ML] Make a tutorial for data preprocessing Key: IGNITE-8741 URL: https://issues.apache.org/jira/browse/IGNITE-8741 Project: Ignite Issue Type: Wish Components: ml Reporter: Yury Babak Assignee: Aleksey Zinoviev Fix For: 2.6 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8680) Encoding categorical features with OneHotEncoder
Yury Babak created IGNITE-8680: -- Summary: Encoding categorical features with OneHotEncoder Key: IGNITE-8680 URL: https://issues.apache.org/jira/browse/IGNITE-8680 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Aleksey Zinoviev Fix For: 2.6 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8679) Integration with tensorflow datasets
Yury Babak created IGNITE-8679: -- Summary: Integration with tensorflow datasets Key: IGNITE-8679 URL: https://issues.apache.org/jira/browse/IGNITE-8679 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Artem Malykh Fix For: 2.6 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8670) Umbrella: TensorFlow integration
Yury Babak created IGNITE-8670: -- Summary: Umbrella: TensorFlow integration Key: IGNITE-8670 URL: https://issues.apache.org/jira/browse/IGNITE-8670 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Yury Babak Fix For: 2.6 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8669) Model estimation
Yury Babak created IGNITE-8669: -- Summary: Model estimation Key: IGNITE-8669 URL: https://issues.apache.org/jira/browse/IGNITE-8669 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Fix For: 2.6 We want to have the common mechanism for model estimation. For estimation we want to have: * Accuracy/precision/recall * F score * TPR/FRP * ROC AUC -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8668) K-fold cross validation of models
Yury Babak created IGNITE-8668: -- Summary: K-fold cross validation of models Key: IGNITE-8668 URL: https://issues.apache.org/jira/browse/IGNITE-8668 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Fix For: 2.6 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8667) Splitting of dataset to test and training sets
Yury Babak created IGNITE-8667: -- Summary: Splitting of dataset to test and training sets Key: IGNITE-8667 URL: https://issues.apache.org/jira/browse/IGNITE-8667 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Anton Dmitriev Fix For: 2.6 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8666) Add ability of filtering data during datasets creation
Yury Babak created IGNITE-8666: -- Summary: Add ability of filtering data during datasets creation Key: IGNITE-8666 URL: https://issues.apache.org/jira/browse/IGNITE-8666 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Fix For: 2.6 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8665) Umbrella: ML model validation for 2.6 release
Yury Babak created IGNITE-8665: -- Summary: Umbrella: ML model validation for 2.6 release Key: IGNITE-8665 URL: https://issues.apache.org/jira/browse/IGNITE-8665 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Yury Babak Fix For: 2.6 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8664) Encoding categorical features
Yury Babak created IGNITE-8664: -- Summary: Encoding categorical features Key: IGNITE-8664 URL: https://issues.apache.org/jira/browse/IGNITE-8664 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Fix For: 2.6 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8663) L1,L2 normalization
Yury Babak created IGNITE-8663: -- Summary: L1,L2 normalization Key: IGNITE-8663 URL: https://issues.apache.org/jira/browse/IGNITE-8663 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Fix For: 2.6 We want to add L1 and L2 normalization using Model/Trainer API. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8662) Umbrella: ML data preprocessing for 2.6 release
Yury Babak created IGNITE-8662: -- Summary: Umbrella: ML data preprocessing for 2.6 release Key: IGNITE-8662 URL: https://issues.apache.org/jira/browse/IGNITE-8662 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Yury Babak Fix For: 2.6 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8335) TensorFlow integration
Yury Babak created IGNITE-8335: -- Summary: TensorFlow integration Key: IGNITE-8335 URL: https://issues.apache.org/jira/browse/IGNITE-8335 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Yury Babak We want to have a integration with TensorFlow. Currently we think that we will implement our own parameter server(our wrap it) and distribute python code to local nodes with data. This solution should use our datasets and python thin client in local python tasks. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8292) Broken yardstick compilation
Yury Babak created IGNITE-8292: -- Summary: Broken yardstick compilation Key: IGNITE-8292 URL: https://issues.apache.org/jira/browse/IGNITE-8292 Project: Ignite Issue Type: Bug Components: ml, yardstick Affects Versions: 2.5 Reporter: Yury Babak Assignee: Yury Babak -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-8181) Broken javadoc in GA Grid
Yury Babak created IGNITE-8181: -- Summary: Broken javadoc in GA Grid Key: IGNITE-8181 URL: https://issues.apache.org/jira/browse/IGNITE-8181 Project: Ignite Issue Type: Bug Components: ml Reporter: Yury Babak Assignee: Yury Babak -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-7916) GA Grid examples should be ready for auto run on TeamCity
Yury Babak created IGNITE-7916: -- Summary: GA Grid examples should be ready for auto run on TeamCity Key: IGNITE-7916 URL: https://issues.apache.org/jira/browse/IGNITE-7916 Project: Ignite Issue Type: Bug Components: examples, ml Reporter: Yury Babak Assignee: Turik Campbell If we start examples MovieGAExample or OptimizeMakeChangeGAExample on TC, this examples will return exit code 1. TeamCity think that it's a error and mark stop whole build of examples package. That behavior should be changed. If we don't have required system properties we should not return exit code 1 or maybe set and use some default values. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-7877) Improve code style in GA part
Yury Babak created IGNITE-7877: -- Summary: Improve code style in GA part Key: IGNITE-7877 URL: https://issues.apache.org/jira/browse/IGNITE-7877 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Not all code in which located in genetic package follows your code style. That should be fixed. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-7741) Fix javadoc for QR factorization
Yury Babak created IGNITE-7741: -- Summary: Fix javadoc for QR factorization Key: IGNITE-7741 URL: https://issues.apache.org/jira/browse/IGNITE-7741 Project: Ignite Issue Type: Bug Components: ml Reporter: Yury Babak Wrong javadoc for QR factorization. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-7716) Red selftest in ML examples
Yury Babak created IGNITE-7716: -- Summary: Red selftest in ML examples Key: IGNITE-7716 URL: https://issues.apache.org/jira/browse/IGNITE-7716 Project: Ignite Issue Type: Bug Components: ml Reporter: Yury Babak Assignee: Yury Babak Fix For: 2.5 https://ci.ignite.apache.org/project.html?tab=testDetails=IgniteTests24Java8=1447870893775475761 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-7643) Broken javadoc in partitioned dataset
Yury Babak created IGNITE-7643: -- Summary: Broken javadoc in partitioned dataset Key: IGNITE-7643 URL: https://issues.apache.org/jira/browse/IGNITE-7643 Project: Ignite Issue Type: Task Components: ml Affects Versions: 2.5 Reporter: Yury Babak Assignee: Yury Babak Fix For: 2.5 [22:25:12][Step 7/7] [WARNING] Javadoc Warnings [22:25:12][Step 7/7] [WARNING] /data/teamcity/work/bd85361428dcdb1/examples/src/main/java/org/apache/ignite/examples/ml/dataset/AlgorithmSpecificDatasetExample.java:51: warning - Tag @link: reference not found: AlgorithmSpecificDataset [22:25:12][Step 7/7] [WARNING] /data/teamcity/work/bd85361428dcdb1/examples/src/main/java/org/apache/ignite/examples/ml/dataset/AlgorithmSpecificDatasetExample.java:51: warning - Tag @link: reference not found: AlgorithmSpecificPartitionContext -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-7511) FCM documentation
Yury Babak created IGNITE-7511: -- Summary: FCM documentation Key: IGNITE-7511 URL: https://issues.apache.org/jira/browse/IGNITE-7511 Project: Ignite Issue Type: Task Components: documentation, ml Reporter: Yury Babak Assignee: Oleg Ignatenko Fix For: 2.4 We need to update documentation on readme.io and add section about FCM -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-7509) Adjust "build" and "getting started" sections of documentation for ML module
Yury Babak created IGNITE-7509: -- Summary: Adjust "build" and "getting started" sections of documentation for ML module Key: IGNITE-7509 URL: https://issues.apache.org/jira/browse/IGNITE-7509 Project: Ignite Issue Type: Task Components: documentation, ml Reporter: Yury Babak Assignee: Yury Babak Fix For: 2.4 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-7504) Decision tree documentation
Yury Babak created IGNITE-7504: -- Summary: Decision tree documentation Key: IGNITE-7504 URL: https://issues.apache.org/jira/browse/IGNITE-7504 Project: Ignite Issue Type: Task Components: documentation, ml Reporter: Yury Babak Assignee: Artem Malykh Fix For: 2.4 We want to add Decision tree documentation -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-7503) MLP documentation
Yury Babak created IGNITE-7503: -- Summary: MLP documentation Key: IGNITE-7503 URL: https://issues.apache.org/jira/browse/IGNITE-7503 Project: Ignite Issue Type: Sub-task Components: documentation, ml Reporter: Yury Babak Assignee: Artem Malykh Fix For: 2.4 A need to add documentation about MLP -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-7484) Documentation for new lin reg trainer.
Yury Babak created IGNITE-7484: -- Summary: Documentation for new lin reg trainer. Key: IGNITE-7484 URL: https://issues.apache.org/jira/browse/IGNITE-7484 Project: Ignite Issue Type: Task Components: ml Reporter: Yury Babak -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-7438) LSQR: Sparse Equations and Least Squares for Lin Regression
Yury Babak created IGNITE-7438: -- Summary: LSQR: Sparse Equations and Least Squares for Lin Regression Key: IGNITE-7438 URL: https://issues.apache.org/jira/browse/IGNITE-7438 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Anton Dmitriev We to implemet LSQR trainer for lin regresstion. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-7437) Partition based dataset implementation
Yury Babak created IGNITE-7437: -- Summary: Partition based dataset implementation Key: IGNITE-7437 URL: https://issues.apache.org/jira/browse/IGNITE-7437 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Anton Dmitriev We want to implement our dataset based on entire partition instead of key sets. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Created] (IGNITE-7350) Distributed MLP cleanup/refactoring
Yury Babak created IGNITE-7350: -- Summary: Distributed MLP cleanup/refactoring Key: IGNITE-7350 URL: https://issues.apache.org/jira/browse/IGNITE-7350 Project: Ignite Issue Type: Bug Components: ml Reporter: Yury Babak Assignee: Yury Babak Current state of MLP not so good, so we need improve it and may be rewrite some parts of this NN. -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (IGNITE-7332) Create selftest suite for ml examples
Yury Babak created IGNITE-7332: -- Summary: Create selftest suite for ml examples Key: IGNITE-7332 URL: https://issues.apache.org/jira/browse/IGNITE-7332 Project: Ignite Issue Type: Improvement Components: ml Reporter: Yury Babak Fix For: 2.5 We want to add self test suite for our examples like we have for java8 examples. -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (IGNITE-7297) Javadoc warning for RProp in MLP
Yury Babak created IGNITE-7297: -- Summary: Javadoc warning for RProp in MLP Key: IGNITE-7297 URL: https://issues.apache.org/jira/browse/IGNITE-7297 Project: Ignite Issue Type: Bug Components: ml Reporter: Yury Babak Assignee: Yury Babak Fix For: 2.4 [Step 7/7] [WARNING] /data/teamcity/work/bd85361428dcdb1/modules/ml/src/main/java/org/apache/ignite/ml/nn/updaters/RPropUpdater.java:32: warning - Tag @see: missing final '>': "https://paginas.fe.up.pt/~ee02162/dissertacao/RPROP%20paper.pdf;>https://paginas.fe.up.pt/~ee02162/dissertacao/RPROP%20paper.pdf." [20:56:44][Step 7/7] [WARNING] /data/teamcity/work/bd85361428dcdb1/modules/ml/src/main/java/org/apache/ignite/ml/nn/updaters/RPropUpdaterParams.java:28: warning - Tag @see: missing final '>': "https://paginas.fe.up.pt/~ee02162/dissertacao/RPROP%20paper.pdf;>https://paginas.fe.up.pt/~ee02162/dissertacao/RPROP%20paper.pdf." -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (IGNITE-7242) Broken javadoc for KNN
Yury Babak created IGNITE-7242: -- Summary: Broken javadoc for KNN Key: IGNITE-7242 URL: https://issues.apache.org/jira/browse/IGNITE-7242 Project: Ignite Issue Type: Bug Components: ml Reporter: Yury Babak Assignee: Yury Babak Fix For: 2.4 -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (IGNITE-7213) Empty class descriptions for KNNModelFormat
Yury Babak created IGNITE-7213: -- Summary: Empty class descriptions for KNNModelFormat Key: IGNITE-7213 URL: https://issues.apache.org/jira/browse/IGNITE-7213 Project: Ignite Issue Type: Bug Components: ml Reporter: Yury Babak Assignee: Yury Babak Priority: Critical Fix For: 2.4 Javadoc generation failed if we have classes with empty class-javadoc -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (IGNITE-7205) DataSet API
Yury Babak created IGNITE-7205: -- Summary: DataSet API Key: IGNITE-7205 URL: https://issues.apache.org/jira/browse/IGNITE-7205 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Assignee: Yury Babak We want to create and implement API for Dataset. This should include Dataset, Labeled Dataset, dataset preprocessors(normalizer, filter, mapper, etc...) -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (IGNITE-7174) Local MLP
Yury Babak created IGNITE-7174: -- Summary: Local MLP Key: IGNITE-7174 URL: https://issues.apache.org/jira/browse/IGNITE-7174 Project: Ignite Issue Type: Sub-task Components: ml Reporter: Yury Babak Fix For: 2.4 local version of MLP -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (IGNITE-7169) Missed javadoc for IgniteToDoubleFunction
Yury Babak created IGNITE-7169: -- Summary: Missed javadoc for IgniteToDoubleFunction Key: IGNITE-7169 URL: https://issues.apache.org/jira/browse/IGNITE-7169 Project: Ignite Issue Type: Bug Components: ml Reporter: Yury Babak Assignee: Yury Babak Fix For: 2.4 Missed javadoc for IgniteToDoubleFunction -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (IGNITE-7150) Gradient boosting for lin regression
Yury Babak created IGNITE-7150: -- Summary: Gradient boosting for lin regression Key: IGNITE-7150 URL: https://issues.apache.org/jira/browse/IGNITE-7150 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak Currently for linreg we have only the analytical trainer (via QR decomposition). We want to add new trainer(implementation of Trainer interface) based on gradient boosting. -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (IGNITE-7149) Gradient boosting for decision tree
Yury Babak created IGNITE-7149: -- Summary: Gradient boosting for decision tree Key: IGNITE-7149 URL: https://issues.apache.org/jira/browse/IGNITE-7149 Project: Ignite Issue Type: New Feature Components: ml Reporter: Yury Babak We want to implement gradient boosting for decision trees. It should be new implementation of Trainer interface and we should keep possibility to choose which trainer we want to use for our tree. -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (IGNITE-7096) Missed dependencies in examples pom files
Yury Babak created IGNITE-7096: -- Summary: Missed dependencies in examples pom files Key: IGNITE-7096 URL: https://issues.apache.org/jira/browse/IGNITE-7096 Project: Ignite Issue Type: Bug Reporter: Yury Babak Assignee: Yury Babak Fix For: 2.4 Missed dependencies in ML profiles in pom-standalone.xml and pom-standalone-lgpl.xml. missed: commons-cli commons-cli 1.2 -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (IGNITE-6949) Cleanup OLS code
Yury Babak created IGNITE-6949: -- Summary: Cleanup OLS code Key: IGNITE-6949 URL: https://issues.apache.org/jira/browse/IGNITE-6949 Project: Ignite Issue Type: Bug Security Level: Public (Viewable by anyone) Reporter: Yury Babak Assignee: Aleksey Zinoviev Fix For: 2.4 We want fix wrong styles like wildcards in imports, unnecessary empty lines, missed empty lines and if-else blocks format in OLS related files. -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (IGNITE-6899) Adding GA Grid to Apache Ignite ML module.
Yury Babak created IGNITE-6899: -- Summary: Adding GA Grid to Apache Ignite ML module. Key: IGNITE-6899 URL: https://issues.apache.org/jira/browse/IGNITE-6899 Project: Ignite Issue Type: New Feature Security Level: Public (Viewable by anyone) Components: ml Reporter: Yury Babak Fix For: 2.4 We want to add GA Grid to our ML Module. This is the first iteration of this integration. On this step we will simple add GA Grid to the separate package in ML module. (i) This is a good package for GA Grid: org.apache.ignite.ml.genetic (i) For GA Grid we need unit tests as well as examples -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (IGNITE-6884) Implement of tensorFold() and tensorProduct() for vectors and matrices
Yury Babak created IGNITE-6884: -- Summary: Implement of tensorFold() and tensorProduct() for vectors and matrices Key: IGNITE-6884 URL: https://issues.apache.org/jira/browse/IGNITE-6884 Project: Ignite Issue Type: Improvement Security Level: Public (Viewable by anyone) Reporter: Yury Babak We want to implement tensor fold and map for matrices and vectors. Also we must take into consideration the different types of matrices vectors, including distribution versions. -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (IGNITE-6882) Introduction of computation graph
Yury Babak created IGNITE-6882: -- Summary: Introduction of computation graph Key: IGNITE-6882 URL: https://issues.apache.org/jira/browse/IGNITE-6882 Project: Ignite Issue Type: New Feature Security Level: Public (Viewable by anyone) Components: ml Reporter: Yury Babak Assignee: Yury Babak Fix For: 2.4 We want to implement a computation graph for NNs because this should helps us achieve for neural networks not only data parallelism but model parallelism too. -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (IGNITE-6880) KNN(k nearest neighbor) algorithm
Yury Babak created IGNITE-6880: -- Summary: KNN(k nearest neighbor) algorithm Key: IGNITE-6880 URL: https://issues.apache.org/jira/browse/IGNITE-6880 Project: Ignite Issue Type: New Feature Security Level: Public (Viewable by anyone) Components: ml Reporter: Yury Babak We want to add KNN to Apache Ignite ML module. Our implementation should support two modes: * KNN-classifier(in this mode the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor) * KNN-regression(the output is the property value for the object. This value is the average of the values of its k nearest neighbors.) -- This message was sent by Atlassian JIRA (v6.4.14#64029)
