RE: Feedback: Feature request
This is great and much appreciated. Thank you.
- Jim
From: Manish Amde [mailto:manish...@gmail.com]
Sent: Friday, August 28, 2015 9:20 AM
To: Cody Koeninger
Cc: Murphy, James; user@spark.apache.org; d...@spark.apache.org
Subject: Re: Feedback: Feature request
Sounds good. It's a request I have seen a few times in the past and have needed
it personally. May be Joseph Bradley has something to add.
I think a JIRA to capture this will be great. We can move this discussion to
the JIRA then.
On Friday, August 28, 2015, Cody Koeninger
c...@koeninger.orgmailto:c...@koeninger.org wrote:
I wrote some code for this a while back, pretty sure it didn't need access to
anything private in the decision tree / random forest model. If people want it
added to the api I can put together a PR.
I think it's important to have separately parseable operators / operands
though. E.g
lhs:0,op:=,rhs:-35.0
On Aug 28, 2015 12:03 AM, Manish Amde
manish...@gmail.comjavascript:_e(%7B%7D,'cvml','manish...@gmail.com');
wrote:
Hi James,
It's a good idea. A JSON format is more convenient for visualization though a
little inconvenient to read. How about toJson() method? It might make the mllib
api inconsistent across models though.
You should probably create a JIRA for this.
CC: dev list
-Manish
On Aug 26, 2015, at 11:29 AM, Murphy, James
james.mur...@disney.comjavascript:_e(%7B%7D,'cvml','james.mur...@disney.com');
wrote:
Hey all,
In working with the DecisionTree classifier, I found it difficult to extract
rules that could easily facilitate visualization with libraries like D3.
So for example, using : print(model.toDebugString()), I get the following
result =
If (feature 0 = -35.0)
If (feature 24 = 176.0)
Predict: 2.1
If (feature 24 = 176.0)
Predict: 4.2
Else (feature 24 176.0)
Predict: 6.3
Else (feature 0 -35.0)
If (feature 24 = 11.0)
Predict: 4.5
Else (feature 24 11.0)
Predict: 10.2
But ideally, I could see results in a more parseable format like JSON:
{
node: [
{
name:node1,
rule:feature 0 = -35.0,
children:[
{
name:node2,
rule:feature 24 = 176.0,
children:[
{
name:node4,
rule:feature 20 116.0,
predict: 2.1
},
{
name:node5,
rule:feature 20 = 116.0,
predict: 4.2
},
{
name:node5,
rule:feature 20 116.0,
predict: 6.3
}
]
},
{
name:node3,
rule:feature 0 -35.0,
children:[
{
name:node7,
rule:feature 3 = 11.0,
predict: 4.5
},
{
name:node8,
rule:feature 3 11.0,
predict: 10.2
}
]
}
]
}
]
}
Food for thought!
Thanks,
Jim
Re: Feedback: Feature request
I wrote some code for this a while back, pretty sure it didn't need access
to anything private in the decision tree / random forest model. If people
want it added to the api I can put together a PR.
I think it's important to have separately parseable operators / operands
though. E.g
lhs:0,op:=,rhs:-35.0
On Aug 28, 2015 12:03 AM, Manish Amde manish...@gmail.com wrote:
Hi James,
It's a good idea. A JSON format is more convenient for visualization
though a little inconvenient to read. How about toJson() method? It might
make the mllib api inconsistent across models though.
You should probably create a JIRA for this.
CC: dev list
-Manish
On Aug 26, 2015, at 11:29 AM, Murphy, James james.mur...@disney.com
wrote:
Hey all,
In working with the DecisionTree classifier, I found it difficult to
extract rules that could easily facilitate visualization with libraries
like D3.
So for example, using : print(model.toDebugString()), I get the following
result =
If (feature 0 = -35.0)
If (feature 24 = 176.0)
Predict: 2.1
If (feature 24 = 176.0)
Predict: 4.2
Else (feature 24 176.0)
Predict: 6.3
Else (feature 0 -35.0)
If (feature 24 = 11.0)
Predict: 4.5
Else (feature 24 11.0)
Predict: 10.2
But ideally, I could see results in a more parseable format like JSON:
{
node: [
{
name:node1,
rule:feature 0 = -35.0,
children:[
{
name:node2,
rule:feature 24 = 176.0,
children:[
{
name:node4,
rule:feature 20 116.0,
predict: 2.1
},
{
name:node5,
rule:feature 20 = 116.0,
predict: 4.2
},
{
name:node5,
rule:feature 20 116.0,
predict: 6.3
}
]
},
{
name:node3,
rule:feature 0 -35.0,
children:[
{
name:node7,
rule:feature 3 = 11.0,
predict: 4.5
},
{
name:node8,
rule:feature 3 11.0,
predict: 10.2
}
]
}
]
}
]
}
Food for thought!
Thanks,
Jim
Re: Feedback: Feature request
Sounds good. It's a request I have seen a few times in the past and have
needed it personally. May be Joseph Bradley has something to add.
I think a JIRA to capture this will be great. We can move this discussion
to the JIRA then.
On Friday, August 28, 2015, Cody Koeninger c...@koeninger.org wrote:
I wrote some code for this a while back, pretty sure it didn't need access
to anything private in the decision tree / random forest model. If people
want it added to the api I can put together a PR.
I think it's important to have separately parseable operators / operands
though. E.g
lhs:0,op:=,rhs:-35.0
On Aug 28, 2015 12:03 AM, Manish Amde manish...@gmail.com
javascript:_e(%7B%7D,'cvml','manish...@gmail.com'); wrote:
Hi James,
It's a good idea. A JSON format is more convenient for visualization
though a little inconvenient to read. How about toJson() method? It might
make the mllib api inconsistent across models though.
You should probably create a JIRA for this.
CC: dev list
-Manish
On Aug 26, 2015, at 11:29 AM, Murphy, James james.mur...@disney.com
javascript:_e(%7B%7D,'cvml','james.mur...@disney.com'); wrote:
Hey all,
In working with the DecisionTree classifier, I found it difficult to
extract rules that could easily facilitate visualization with libraries
like D3.
So for example, using : print(model.toDebugString()), I get the following
result =
If (feature 0 = -35.0)
If (feature 24 = 176.0)
Predict: 2.1
If (feature 24 = 176.0)
Predict: 4.2
Else (feature 24 176.0)
Predict: 6.3
Else (feature 0 -35.0)
If (feature 24 = 11.0)
Predict: 4.5
Else (feature 24 11.0)
Predict: 10.2
But ideally, I could see results in a more parseable format like JSON:
{
node: [
{
name:node1,
rule:feature 0 = -35.0,
children:[
{
name:node2,
rule:feature 24 = 176.0,
children:[
{
name:node4,
rule:feature 20 116.0,
predict: 2.1
},
{
name:node5,
rule:feature 20 = 116.0,
predict: 4.2
},
{
name:node5,
rule:feature 20 116.0,
predict: 6.3
}
]
},
{
name:node3,
rule:feature 0 -35.0,
children:[
{
name:node7,
rule:feature 3 = 11.0,
predict: 4.5
},
{
name:node8,
rule:feature 3 11.0,
predict: 10.2
}
]
}
]
}
]
}
Food for thought!
Thanks,
Jim
Re: Feedback: Feature request
Hi James,
It's a good idea. A JSON format is more convenient for visualization though a
little inconvenient to read. How about toJson() method? It might make the mllib
api inconsistent across models though.
You should probably create a JIRA for this.
CC: dev list
-Manish
On Aug 26, 2015, at 11:29 AM, Murphy, James james.mur...@disney.com wrote:
Hey all,
In working with the DecisionTree classifier, I found it difficult to extract
rules that could easily facilitate visualization with libraries like D3.
So for example, using : print(model.toDebugString()), I get the following
result =
If (feature 0 = -35.0)
If (feature 24 = 176.0)
Predict: 2.1
If (feature 24 = 176.0)
Predict: 4.2
Else (feature 24 176.0)
Predict: 6.3
Else (feature 0 -35.0)
If (feature 24 = 11.0)
Predict: 4.5
Else (feature 24 11.0)
Predict: 10.2
But ideally, I could see results in a more parseable format like JSON:
{
node: [
{
name:node1,
rule:feature 0 = -35.0,
children:[
{
name:node2,
rule:feature 24 = 176.0,
children:[
{
name:node4,
rule:feature 20 116.0,
predict: 2.1
},
{
name:node5,
rule:feature 20 = 116.0,
predict: 4.2
},
{
name:node5,
rule:feature 20 116.0,
predict: 6.3
}
]
},
{
name:node3,
rule:feature 0 -35.0,
children:[
{
name:node7,
rule:feature 3 = 11.0,
predict: 4.5
},
{
name:node8,
rule:feature 3 11.0,
predict: 10.2
}
]
}
]
}
]
}
Food for thought!
Thanks,
Jim
Feedback: Feature request
Hey all,
In working with the DecisionTree classifier, I found it difficult to extract
rules that could easily facilitate visualization with libraries like D3.
So for example, using : print(model.toDebugString()), I get the following
result =
If (feature 0 = -35.0)
If (feature 24 = 176.0)
Predict: 2.1
If (feature 24 = 176.0)
Predict: 4.2
Else (feature 24 176.0)
Predict: 6.3
Else (feature 0 -35.0)
If (feature 24 = 11.0)
Predict: 4.5
Else (feature 24 11.0)
Predict: 10.2
But ideally, I could see results in a more parseable format like JSON:
{
node: [
{
name:node1,
rule:feature 0 = -35.0,
children:[
{
name:node2,
rule:feature 24 = 176.0,
children:[
{
name:node4,
rule:feature 20 116.0,
predict: 2.1
},
{
name:node5,
rule:feature 20 = 116.0,
predict: 4.2
},
{
name:node5,
rule:feature 20 116.0,
predict: 6.3
}
]
},
{
name:node3,
rule:feature 0 -35.0,
children:[
{
name:node7,
rule:feature 3 = 11.0,
predict: 4.5
},
{
name:node8,
rule:feature 3 11.0,
predict: 10.2
}
]
}
]
}
]
}
Food for thought!
Thanks,
Jim
