[jira] [Commented] (SPARK-7258) spark.ml API taking Graph instead of DataFrame

["zhengruifeng (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-7258?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15249509#comment-15249509 ] 

zhengruifeng commented on SPARK-7258:
-------------------------------------

I have only encounter Graph-like API in Keras, within which I construct some complex networks.
Keras had two modeling API: Sequential (like Pipelines, add layers one by one), Graph (add nodes).
Its Graph API looks like this:
{code}
graph = Graph()  
graph.add_input(name='input1', input_shape=(32,))  
graph.add_input(name='input2', input_shape=(32,))  
graph.add_node(Dense(16), name='dense1', input='input1')  
graph.add_node(Dense(4), name='dense2', input='input2')  
graph.add_node(Dense(4), name='dense3', input='dense1')  
graph.add_output(name='output', inputs=['dense2', 'dense3'], merge_mode='sum')  
graph.compile('rmsprop', {'output':'mse'})  
history = graph.fit({'input1':X_train, 'input2':X2_train, 'output':y_train}, nb_epoch=10)  
predictions = graph.predict({'input1':X_test, 'input2':X2_test})
{code}

And the perhaps API may look like this:
{code}
val graph = Graph()
graph.addInput(name="input1", path='...')
graph.addInput(name="input2", path='...')
graph.addSQLNode(name="sql", inputs=["input1", "input2"], sql="select * from  ... join ...")
graph.addNode(name="tfidf", transformer=...)
...
graph.addOutput(name="output")
{code}

> spark.ml API taking Graph instead of DataFrame
> ----------------------------------------------
>
>                 Key: SPARK-7258
>                 URL: https://issues.apache.org/jira/browse/SPARK-7258
>             Project: Spark
>          Issue Type: New Feature
>          Components: GraphX, ML
>            Reporter: Joseph K. Bradley
>
> It would be useful to have an API in ML Pipelines for working with Graphs, not just DataFrames.



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