[jira] [Commented] (MADLIB-1102) Graph - Breadth First Search / Traversal
[ https://issues.apache.org/jira/browse/MADLIB-1102?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16084890#comment-16084890 ] ASF GitHub Bot commented on MADLIB-1102: Github user rashmi815 closed the pull request at: https://github.com/apache/incubator-madlib/pull/141 > Graph - Breadth First Search / Traversal > > > Key: MADLIB-1102 > URL: https://issues.apache.org/jira/browse/MADLIB-1102 > Project: Apache MADlib > Issue Type: New Feature > Components: Module: Graph >Reporter: Rashmi Raghu >Assignee: Rashmi Raghu > Fix For: v1.12 > > > Story > As a MADlib user and developer, I want to implement Breadth First Search / > Traversal for a graph. BFS is also a core part of the connected components > graph algorithm. > Accpetance: > 1) Interface defined > 2) Design doc updated > 3) Documentation and on-line help > 4) IC and functional tests > 5) Scale tests > References: > [0] [https://en.wikipedia.org/wiki/Breadth-first_search] > "Breadth-first search (BFS) is an algorithm for traversing or searching tree > or graph data structures. It starts at the tree root (or some arbitrary node > of a graph, sometimes referred to as a 'search key'[1]) and explores the > neighbor nodes first, before moving to the next level neighbors." > [1] [http://www.geeksforgeeks.org/breadth-first-traversal-for-a-graph/] -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Assigned] (MADLIB-1135) Neural Networks - MLP - Phase 3
[ https://issues.apache.org/jira/browse/MADLIB-1135?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Frank McQuillan reassigned MADLIB-1135: --- Assignee: (was: Cooper Sloan) > Neural Networks - MLP - Phase 3 > --- > > Key: MADLIB-1135 > URL: https://issues.apache.org/jira/browse/MADLIB-1135 > Project: Apache MADlib > Issue Type: Improvement > Components: Module: Neural Networks >Reporter: Frank McQuillan > Fix For: v1.12 > > > Follow on from https://issues.apache.org/jira/browse/MADLIB-413 > Story > As a MADlib developer, I want to get 2nd phase implementation of NN going > with training and prediction functions, so that I can use this to build to an > MVP version for GA. > Features to add: > * weights for inputs > * logic for n_tries > * multiple loss functions > * normalize inputs > * L2 regularization -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (MADLIB-1134) Neural Networks - MLP - Phase 2
[ https://issues.apache.org/jira/browse/MADLIB-1134?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Frank McQuillan updated MADLIB-1134: Issue Type: Improvement (was: New Feature) > Neural Networks - MLP - Phase 2 > --- > > Key: MADLIB-1134 > URL: https://issues.apache.org/jira/browse/MADLIB-1134 > Project: Apache MADlib > Issue Type: Improvement > Components: Module: Neural Networks >Reporter: Frank McQuillan >Assignee: Cooper Sloan > Fix For: v1.12 > > > Follow on from https://issues.apache.org/jira/browse/MADLIB-413 > Story > As a MADlib developer, I want to get 2nd phase implementation of NN going > with training and prediction functions, so that I can use this to build to an > MVP version for GA. > Features to add > * weights for inputs > * logic for n_tries > * multiple loss functions > * normalize inputs > * L2 regularization -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (MADLIB-1135) Neural Networks - MLP - Phase 3
[ https://issues.apache.org/jira/browse/MADLIB-1135?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Frank McQuillan updated MADLIB-1135: Description: Follow on from https://issues.apache.org/jira/browse/MADLIB-413 and https://issues.apache.org/jira/browse/MADLIB-1134 Story As a MADlib developer, I want to get 3nd phase implementation of NN going with training and prediction functions, so that I can have a more advanced and performant version of NN Features to add: * other algos (e.g., resilient backpropagation) * momentum was: Follow on from https://issues.apache.org/jira/browse/MADLIB-413 Story As a MADlib developer, I want to get 2nd phase implementation of NN going with training and prediction functions, so that I can use this to build to an MVP version for GA. Features to add: * weights for inputs * logic for n_tries * multiple loss functions * normalize inputs * L2 regularization > Neural Networks - MLP - Phase 3 > --- > > Key: MADLIB-1135 > URL: https://issues.apache.org/jira/browse/MADLIB-1135 > Project: Apache MADlib > Issue Type: Improvement > Components: Module: Neural Networks >Reporter: Frank McQuillan > Fix For: v2.0 > > > Follow on from https://issues.apache.org/jira/browse/MADLIB-413 and > https://issues.apache.org/jira/browse/MADLIB-1134 > Story > As a MADlib developer, I want to get 3nd phase implementation of NN going > with training and prediction functions, so that I can have a more advanced > and performant version of NN > Features to add: > * other algos (e.g., resilient backpropagation) > * momentum -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (MADLIB-1135) Neural Networks - MLP - Phase 3
[ https://issues.apache.org/jira/browse/MADLIB-1135?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Frank McQuillan updated MADLIB-1135: Fix Version/s: (was: v1.12) v2.0 > Neural Networks - MLP - Phase 3 > --- > > Key: MADLIB-1135 > URL: https://issues.apache.org/jira/browse/MADLIB-1135 > Project: Apache MADlib > Issue Type: Improvement > Components: Module: Neural Networks >Reporter: Frank McQuillan > Fix For: v2.0 > > > Follow on from https://issues.apache.org/jira/browse/MADLIB-413 and > https://issues.apache.org/jira/browse/MADLIB-1134 > Story > As a MADlib developer, I want to get 3nd phase implementation of NN going > with training and prediction functions, so that I can have a more advanced > and performant version of NN > Features to add: > * other algos (e.g., resilient backpropagation) > * momentum -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (MADLIB-1135) Neural Networks - MLP - Phase 3
Frank McQuillan created MADLIB-1135: --- Summary: Neural Networks - MLP - Phase 3 Key: MADLIB-1135 URL: https://issues.apache.org/jira/browse/MADLIB-1135 Project: Apache MADlib Issue Type: Improvement Components: Module: Neural Networks Reporter: Frank McQuillan Assignee: Cooper Sloan Fix For: v1.12 Follow on from https://issues.apache.org/jira/browse/MADLIB-413 Story As a MADlib developer, I want to get 2nd phase implementation of NN going with training and prediction functions, so that I can use this to build to an MVP version for GA. Features to add: * weights for inputs * logic for n_tries * multiple loss functions * normalize inputs * L2 regularization -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (MADLIB-1134) Neural Networks - MLP - Phase 2
[ https://issues.apache.org/jira/browse/MADLIB-1134?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Frank McQuillan updated MADLIB-1134: Reporter: Frank McQuillan (was: Caleb Welton) > Neural Networks - MLP - Phase 2 > --- > > Key: MADLIB-1134 > URL: https://issues.apache.org/jira/browse/MADLIB-1134 > Project: Apache MADlib > Issue Type: New Feature > Components: Module: Neural Networks >Reporter: Frank McQuillan >Assignee: Cooper Sloan > Fix For: v1.12 > > > Follow on from https://issues.apache.org/jira/browse/MADLIB-413 > Story > As a MADlib developer, I want to get 2nd phase implementation of NN going > with training and prediction functions, so that I can use this to build to an > MVP version for GA. > Features to add > * weights for inputs > * logic for n_tries > * multiple loss functions > * normalize inputs > * L2 regularization -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (MADLIB-1134) Neural Networks - MLP - Phase 2
[
https://issues.apache.org/jira/browse/MADLIB-1134?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Frank McQuillan updated MADLIB-1134:
Description:
Follow on from https://issues.apache.org/jira/browse/MADLIB-413
Story
As a MADlib developer, I want to get 2nd phase implementation of NN going with
training and prediction functions, so that I can use this to build to an MVP
version for GA.
Features to add
* weights for inputs
* logic for n_tries
* multiple loss functions
* normalize inputs
* L2 regularization
was:
Multilayer perceptron with backpropagation
Modules:
* mlp_classification
* mlp_regression
Interface
{code}
source_table VARCHAR
output_table VARCHAR
independent_varname VARCHAR -- Column name for input features, should be a Real
Valued array
dependent_varname VARCHAR, -- Column name for target values, should be Real
Valued array of size 1 or greater
hidden_layer_sizes INTEGER[], -- Number of units per hidden layer (can be empty
or null, in which case, no hidden layers)
optimizer_params VARCHAR, -- Specified below
weights VARCHAR, -- Column name for weights. Weights the loss for each input
vector. Column should contain positive real value
activation_function VARCHAR, -- One of 'sigmoid' (default), 'tanh', 'relu', or
any prefix (eg. 't', 's')
grouping_cols
)
{code}
where
{code}
optimizer_params: -- eg "step_size=0.5, n_tries=5"
{
step_size DOUBLE PRECISION, -- Learning rate
n_iterations INTEGER, -- Number of iterations per try
n_tries INTEGER, -- Total number of training cycles, with random
initializations to avoid local minima.
tolerance DOUBLE PRECISION, -- Maximum distance between weights before training
stops (or until it reaches n_iterations)
}
{code}
> Neural Networks - MLP - Phase 2
> ---
>
> Key: MADLIB-1134
> URL: https://issues.apache.org/jira/browse/MADLIB-1134
> Project: Apache MADlib
> Issue Type: New Feature
> Components: Module: Neural Networks
>Reporter: Caleb Welton
>Assignee: Cooper Sloan
> Fix For: v1.12
>
>
> Follow on from https://issues.apache.org/jira/browse/MADLIB-413
> Story
> As a MADlib developer, I want to get 2nd phase implementation of NN going
> with training and prediction functions, so that I can use this to build to an
> MVP version for GA.
> Features to add
> * weights for inputs
> * logic for n_tries
> * multiple loss functions
> * normalize inputs
> * L2 regularization
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[jira] [Updated] (MADLIB-1134) Neural Networks - MLP - Phase 2
[
https://issues.apache.org/jira/browse/MADLIB-1134?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Frank McQuillan updated MADLIB-1134:
Summary: Neural Networks - MLP - Phase 2 (was: CLONE - Neural Networks -
MLP - Phase 1)
> Neural Networks - MLP - Phase 2
> ---
>
> Key: MADLIB-1134
> URL: https://issues.apache.org/jira/browse/MADLIB-1134
> Project: Apache MADlib
> Issue Type: New Feature
> Components: Module: Neural Networks
>Reporter: Caleb Welton
>Assignee: Cooper Sloan
> Fix For: v1.12
>
>
> Multilayer perceptron with backpropagation
> Modules:
> * mlp_classification
> * mlp_regression
> Interface
> {code}
> source_table VARCHAR
> output_table VARCHAR
> independent_varname VARCHAR -- Column name for input features, should be a
> Real Valued array
> dependent_varname VARCHAR, -- Column name for target values, should be Real
> Valued array of size 1 or greater
> hidden_layer_sizes INTEGER[], -- Number of units per hidden layer (can be
> empty or null, in which case, no hidden layers)
> optimizer_params VARCHAR, -- Specified below
> weights VARCHAR, -- Column name for weights. Weights the loss for each input
> vector. Column should contain positive real value
> activation_function VARCHAR, -- One of 'sigmoid' (default), 'tanh', 'relu',
> or any prefix (eg. 't', 's')
> grouping_cols
> )
> {code}
> where
> {code}
> optimizer_params: -- eg "step_size=0.5, n_tries=5"
> {
> step_size DOUBLE PRECISION, -- Learning rate
> n_iterations INTEGER, -- Number of iterations per try
> n_tries INTEGER, -- Total number of training cycles, with random
> initializations to avoid local minima.
> tolerance DOUBLE PRECISION, -- Maximum distance between weights before
> training stops (or until it reaches n_iterations)
> }
> {code}
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[jira] [Created] (MADLIB-1134) CLONE - Neural Networks - MLP - Phase 1
Frank McQuillan created MADLIB-1134:
---
Summary: CLONE - Neural Networks - MLP - Phase 1
Key: MADLIB-1134
URL: https://issues.apache.org/jira/browse/MADLIB-1134
Project: Apache MADlib
Issue Type: New Feature
Components: Module: Neural Networks
Reporter: Caleb Welton
Assignee: Cooper Sloan
Fix For: v1.12
Multilayer perceptron with backpropagation
Modules:
* mlp_classification
* mlp_regression
Interface
{code}
source_table VARCHAR
output_table VARCHAR
independent_varname VARCHAR -- Column name for input features, should be a Real
Valued array
dependent_varname VARCHAR, -- Column name for target values, should be Real
Valued array of size 1 or greater
hidden_layer_sizes INTEGER[], -- Number of units per hidden layer (can be empty
or null, in which case, no hidden layers)
optimizer_params VARCHAR, -- Specified below
weights VARCHAR, -- Column name for weights. Weights the loss for each input
vector. Column should contain positive real value
activation_function VARCHAR, -- One of 'sigmoid' (default), 'tanh', 'relu', or
any prefix (eg. 't', 's')
grouping_cols
)
{code}
where
{code}
optimizer_params: -- eg "step_size=0.5, n_tries=5"
{
step_size DOUBLE PRECISION, -- Learning rate
n_iterations INTEGER, -- Number of iterations per try
n_tries INTEGER, -- Total number of training cycles, with random
initializations to avoid local minima.
tolerance DOUBLE PRECISION, -- Maximum distance between weights before training
stops (or until it reaches n_iterations)
}
{code}
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[jira] [Updated] (MADLIB-413) Neural Networks - MLP - Phase 1
[
https://issues.apache.org/jira/browse/MADLIB-413?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Frank McQuillan updated MADLIB-413:
---
Summary: Neural Networks - MLP - Phase 1 (was: Neural Networks - MLP)
> Neural Networks - MLP - Phase 1
> ---
>
> Key: MADLIB-413
> URL: https://issues.apache.org/jira/browse/MADLIB-413
> Project: Apache MADlib
> Issue Type: New Feature
> Components: Module: Neural Networks
>Reporter: Caleb Welton
>Assignee: Cooper Sloan
> Fix For: v1.12
>
>
> Multilayer perceptron with backpropagation
> Modules:
> * mlp_classification
> * mlp_regression
> Interface
> {code}
> source_table VARCHAR
> output_table VARCHAR
> independent_varname VARCHAR -- Column name for input features, should be a
> Real Valued array
> dependent_varname VARCHAR, -- Column name for target values, should be Real
> Valued array of size 1 or greater
> hidden_layer_sizes INTEGER[], -- Number of units per hidden layer (can be
> empty or null, in which case, no hidden layers)
> optimizer_params VARCHAR, -- Specified below
> weights VARCHAR, -- Column name for weights. Weights the loss for each input
> vector. Column should contain positive real value
> activation_function VARCHAR, -- One of 'sigmoid' (default), 'tanh', 'relu',
> or any prefix (eg. 't', 's')
> grouping_cols
> )
> {code}
> where
> {code}
> optimizer_params: -- eg "step_size=0.5, n_tries=5"
> {
> step_size DOUBLE PRECISION, -- Learning rate
> n_iterations INTEGER, -- Number of iterations per try
> n_tries INTEGER, -- Total number of training cycles, with random
> initializations to avoid local minima.
> tolerance DOUBLE PRECISION, -- Maximum distance between weights before
> training stops (or until it reaches n_iterations)
> }
> {code}
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