mahout-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From David Rahman <drahman1...@googlemail.com>
Subject Re: confidence values of one (or more) feature(s)
Date Thu, 03 Nov 2011 20:33:02 GMT
Thank you Ted,

I will test the methods next week, when I'm back in the office and let you
know how it went.

Thank you and best regards,
David

2011/11/3 Ted Dunning <ted.dunning@gmail.com>

> OK.
>
> So the simplest design in Mahout terms is a binary classifier for each
> keyword (if the keywords are not mutually exclusive).  If you can define a
> useful ordering for terms or have some logical entailment, you may want to
> allow the presence of some terms to be features for certain other terms.
>
> So the question boils down to how to ask a binary logistic regression how
> it came to its conclusion.
>
> You are correct to look to the model dissector for the function you want,
> but you will have to call it in a little bit unusual way because it is
> really intended to describe a model rather than a single decision.  The
> logistic regression functions in Mahout don't actually expose quite as much
> information as you need for this, but if you add this method, you should
> get the basic information you need:
>
>        /**
>   * Return the element-wise product of the feature vector versus each
> column
>   * of the beta matrix.  This can then be used to extract the most
> interesting
>   * features for a decision for each alternative output.
>   * @param instance  A feature vector
>   * @return   A matrix like beta but with each column multiplied by
> instance.
>   */
>  public Matrix explain(Vector instance) {
>    regularize(instance);
>    Matrix r = beta.like().assign(beta);
>    for (int column = 0; column < r.columnSize(); column++) {
>      r.viewColumn(column).assign(instance, Functions.MULT);
>    }
>    return r;
>  }
>
>
> Then to explain your binary model, you probably want some code like this:
>
>   Map<String, Set<Integer>> traceDictionary = Maps.newHashSet();
>   Vector instance = encode(data, traceDictionary)
>   Matrix b = model.explain(instance);
>
>   ModelDissector md = new ModelDissector();
>   // get positive terms
>   ModelDissector.update(b.getColumn(0), td, model);
>   // scan through the top terms
>   ...
>
>   md = new ModelDissector();
>   ModelDissector.update(b.getColumn(0).assign(Functions.NEGATE), td,
> model);
>   // scan through the most negative terms
>   ...
>
> Note that all of this code is untested and I could be out to lunch here.
>
>
>
>
> On Thu, Nov 3, 2011 at 12:19 PM, David Rahman <drahman1985@googlemail.com
> >wrote:
>
> > Hi Ted,
> >
> > I want to have the model explain why it classified documents in a certain
> > way. That should be enough at first.
> >
> > I want to classify documents, each document has a corresponding set of
> > keywords. The model should be able to classify unknown documents and
> > provide a number of suggustions of keywords. Later on it should be
> possible
> > to build a search term recommender for a search engine with classified
> > documents as a basis.
> >
> > At first we wanted to use the lucene data, but the existing data is build
> > with an older lucene version, so the data is provided in xml, for now.
> It's
> > like the wikipedia example, only with more possible keywords.
> >
> > Hope it's understandable.
> >
> > Thanks for your endurance and regards,
> > David
> >
> > 2011/11/3 Ted Dunning <ted.dunning@gmail.com>
> >
> > > I am sorry for being dense, but I don't really understand what you are
> > > trying to do.
> > >
> > > As I see it,
> > >
> > > - the input is documents
> > >
> > > - the output is a category
> > >
> > > You want one or more of the following,
> > >
> > > - to have the model explain why it classified documents a certain way
> > >
> > > or
> > >
> > > - to classify non-document phrases a certain way
> > >
> > > or
> > >
> > > - to have the model show its internal structure to you
> > >
> > > or
> > >
> > > - something else entirely
> > >
> > > Can you say what you want in these terms?
> > >
> > > On Thu, Nov 3, 2011 at 8:43 AM, David Rahman <
> drahman1985@googlemail.com
> > > >wrote:
> > >
> > > > Hi Ted,
> > > >
> > > > thank you for the explanation.
> > > > For example imagine a term cloud, in which terms are presented. Some
> > > terms
> > > > are bigger than other, because they are more likely than the other
> > > terms. I
> > > > would need those results for analysis. We want to compare different
> > > > ML-algorithms and methods and/or compinations of them. And first I
> have
> > > to
> > > > gain some basic knowledge about Mahout.
> > > >
> > > > For example, when I take the word 'social' as input I'd like to have
> > that
> > > > result:
> > > >
> > > > social                    1.0
> > > > social media           0.8
> > > > social networking    0.65
> > > > social news            0.6
> > > > facebook                0.5
> > > > ...
> > > >
> > > > (ignore those values, it's not correct, but it should show what I
> need)
> > > >
> > > > The 20Newsgroup-example shows with the summary(int n) method the most
> > > > likely categorisation of a term (--> the most important feature). I
> > would
> > > > like to have a list with the second, third, and so on important
> > feature.
> > > I
> > > > imagine, while computing the features, only the most import ones are
> > > added
> > > > to the list and the less important features are rejected.
> > > >
> > > > Thanks and regards,
> > > > David
> > > >
> > > > 2011/11/3 Ted Dunning <ted.dunning@gmail.com>
> > > >
> > > > > There are no confidence values per se in the models computed by
> > Mahout
> > > at
> > > > > this time.
> > > > >
> > > > > There are several issues here,
> > > > >
> > > > > 1) Naive Bayes doesn't have such a concept.  'Nuff said there.
> > > > >
> > > > > 2) SGD logistic regresssion could compute confidence intervals,
> but I
> > > am
> > > > > not quite sure how to do that with stochastic gradient descent.
> > > > >
> > > > > 3) in most uses of Mahout's logistic regression, the issues are
> data
> > > size
> > > > > and feature set size.  Confidence values are typically used for
> > > selecting
> > > > > features which is typically not a viable strategy for problems with
> > > very
> > > > > large feature sets.  That is what the L1 regularization is all
> about.
> > > > >
> > > > > 4) with an extremely large number features, the noise on confidence
> > > > > intervals makes them very hard to understand
> > > > >
> > > > > 5) with hashed features and feature collisions it is hard enough
to
> > > > > understand which feature is doing what, much less what the
> confidence
> > > > > interval means.
> > > > >
> > > > > Can you say more about your problem?  Is it small enough to use
> > > bayesglm
> > > > in
> > > > > R?
> > > > >
> > > > > On Thu, Nov 3, 2011 at 7:25 AM, David Rahman <
> > > drahman1985@googlemail.com
> > > > > >wrote:
> > > > >
> > > > > > Me again,
> > > > > >
> > > > > > can someone point me to right direction? How can I access these
> > > > features?
> > > > > > I looked into the summary(int n) -method located in
> > > > > > org.apache.mahout.classifier.sgd.Modeldissector.java, but
> somehow I
> > > > don't
> > > > > > understand how it works.
> > > > > >
> > > > > > Could someone explain to me how it works? As I understand it,
it
> > > > returns
> > > > > > just the max-value of a feature.
> > > > > >
> > > > > > Thanks and regards,
> > > > > > David
> > > > > >
> > > > > > 2011/10/20 David Rahman <drahman1985@googlemail.com>
> > > > > >
> > > > > > > Hi,
> > > > > > >
> > > > > > > how can I access the confidence values of one (or more)
> > feature(s)
> > > > with
> > > > > > > its possibilities?
> > > > > > >
> > > > > > > In the 20Newsgroup-example, there is the dissect method,
within
> > > there
> > > > > is
> > > > > > > used summary(int n), which returns the n most important
> features
> > > with
> > > > > > their
> > > > > > > weights. I want also the features which are placed second
or
> > third
> > > > (or
> > > > > > > more). How can I access those?
> > > > > > >
> > > > > > > Regards,
> > > > > > > David
> > > > > > >
> > > > > >
> > > > >
> > > >
> > >
> >
>

Mime
  • Unnamed multipart/alternative (inline, None, 0 bytes)
View raw message