[scikit-learn] I’m in trouble and I need your advice on operating scikit-learn

[David Nicholson]

Hey Jinwoo,

Sounds like you're in a tough situation.
Not sure why people are responding with discussions of the true meaning of
AI.

As far as getting things out of xml goes, you can use the module ElemenTree
that's in the standard Python library.
https://eli.thegreenplace.net/2012/03/15/processing-xml-in-python-with-elementtree/

As far as learning how to write code and what import statements, there's a
lot of free resources on the web:
https://github.com/jakevdp/PythonDataScienceHandbook
There's also video tutorials on YouTube such as
https://youtu.be/2kT6QOVSgSg

I don't want to speak for others but I think on this listserv would not be
the best place to get help with learning how to write code and what import
statements to use. You might start with a Stack Overflow post and tag it
with 'scikit-learn' if you want to get help faster.

Hope that helps,
David


On Thu, Mar 29, 2018 at 4:50 AM, Guillaume Lemaître <g.lemaitre58 at gmail.com>
wrote:

> > However, these are typically considered "machine learning" techniques;
> when someone says "AI", they typically mean a Neural Network.
>
> I am sorry but I disagree: https://en.wikipedia.org/wiki/
> Artificial_intelligence
>
> On 29 March 2018 at 06:47, Andrew Howe <ahowe42 at gmail.com> wrote:
>
>> Hi Jinwoo
>>
>> It is true that scikit-learn has many models for supervised
>> classification tasks, and it should be relatively trivial for you to munge
>> your 3 data files into the X (data) y (labels) format required for these
>> methods.  Examples are k-means, Support Vector Machines, Decision Trees,
>> and Discriminant Analysis.  However, these are typically considered
>> "machine learning" techniques; when someone says "AI", they typically mean
>> a Neural Network.  If you wish to use scikit-learn for Neural Network
>> classification, you are limited to the Multilayer Perceptron:
>> http://scikit-learn.org/stable/modules/neural_networks_supervised.html#.
>> If you want to be able to use more advanced Neural Networks, here are some
>> options:
>>
>> *Deep neural networks etc.*
>>
>>    - pylearn2 <http://deeplearning.net/software/pylearn2/> A deep
>>    learning and neural network library build on theano with scikit-learn like
>>    interface.
>>    - sklearn_theano <http://sklearn-theano.github.io/> scikit-learn
>>    compatible estimators, transformers, and datasets which use Theano
>>    internally
>>    - nolearn <https://github.com/dnouri/nolearn> A number of wrappers
>>    and abstractions around existing neural network libraries
>>    - keras <https://github.com/fchollet/keras> Deep Learning library
>>    capable of running on top of either TensorFlow or Theano.
>>    - lasagne <https://github.com/Lasagne/Lasagne> A lightweight library
>>    to build and train neural networks in Theano.
>>
>> I personally use Google's TensorFlow.  Hope this helps.
>>
>> Andrew
>>
>> <~~~~~~~~~~~~~~~~~~~~~~~~~~~>
>> J. Andrew Howe, PhD
>> LinkedIn Profile <http://www.linkedin.com/in/ahowe42>
>> ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/>
>> Open Researcher and Contributor ID (ORCID)
>> <http://orcid.org/0000-0002-3553-1990>
>> Github Profile <http://github.com/ahowe42>
>> Personal Website <http://www.andrewhowe.com>
>> I live to learn, so I can learn to live. - me
>> <~~~~~~~~~~~~~~~~~~~~~~~~~~~>
>>
>> On Thu, Mar 29, 2018 at 7:06 AM, PARK Jinwoo <jinwoo412 at gmail.com> wrote:
>>
>>> Dear scikit-learn experts
>>>
>>> Hello, I am a graduate school student majoring in doping control
>>> analysis in Korea.
>>> Now I'm in a research institute that carries out doping control analyses.
>>>
>>> I received a project by my advising doctor. It's about operating an AI
>>> project.
>>> A workshop is scheduled in April, so it needs to be done in a month.
>>> However, I haven't learn computer science at all and I'm totally
>>> ignorant of it.
>>> So I desperately need your advice.
>>>
>>> To be specific, the 3 xml files shown in the picture are analysis results
>>> named positive, negative, and unknown from top to bottom.
>>> We'd like to let AI learn positive and negative data,
>>> input unknown datum, and then see what result will turn out.
>>>
>>> I came to know that there's a module called 'iris calssification' in
>>> scikit-learn
>>> and I'm thinking of utilizing that as it seems similar with my assignment
>>> However, while the database of iris is a csv file with 150 data and
>>> labels inside,
>>> what I have are 3 xml files each one of which represents one data,
>>> which are stored in C:\Users\Jinwoo\Documents\Python Scripts\mzdata
>>> The training process is not shuffling randomly the 150 data and
>>> dividing into training set and test set. The data are already assigned
>>> into training ones and testing one.
>>> Also, when training the program, training labels naming positive and
>>> negative should be inserted on my own.
>>>
>>> What I know all is that it will be appropriate to use fit() function
>>> and predict() function to train and test.
>>> But I have no idea on what to import, how to write codes correctly, and
>>> so on
>>>
>>> It will be thankful to give me some help.
>>>
>>> <https://mail.python.org/mailman/listinfo/scikit-learn>
>>> _______________________________________________
>>> scikit-learn mailing list
>>> scikit-learn at python.org
>>> https://mail.python.org/mailman/listinfo/scikit-learn
>>>
>>>
>>
>> _______________________________________________
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>>
>>
>
>
> --
> Guillaume Lemaitre
> INRIA Saclay - Parietal team
> Center for Data Science Paris-Saclay
> https://glemaitre.github.io/
>
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>
>


-- 
David Nicholson, Ph.D.
nickledave.github.io
https://github.com/NickleDave
Sober Lab <http://www.biology.emory.edu/research/Sober/Home.html>
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