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https://issues.apache.org/jira/browse/TIKA-1517?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14281254#comment-14281254
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Luke sh commented on TIKA-1517:
-------------------------------
Basic feature design:
Probability selection mechanism is incorporated only in the MIMEtypes detector
in Tika, There are currently 4 detectors implemented in Tika, they are
"org.gagravarr.tika.OggDetector",
"org.apache.tika.parser.microsoft.POIFSContainerDetector",
"org.apache.tika.parser.pkg.ZipContainerDetector" and lastly the
"org.apache.tika.mime.MimeTypes".
Other than MimeTypes detector, the other 3 detectors make calls to some other
open APIs in detecting the MIME types, if there is a inconsistent MIME type
detected between detectors, Tika will probably make the choice with the type
detected in the preferential order above.
Inside the MimeTypes detector, the probability selection mechanism is invoked
inside the detect() method, the method is called
applyProbilities(List<MimeType>:: possibleTypes, MimeType:: extMimeType,
MimeType:: metadataMimeType).
possibleTypes is a list of MimeTypes estimated by Magic-byte method. It is
possible that Magic-bytes method estimate more than one types, it is also
assumed the list maintain an order of precedence, the first element has higher
weights. applyProbabilities method calculate the posterior probability for each
file type estimated by each type detection method, and keep track the one that
has higher posterior probability and eventually return it as result. it is also
worth noting that a type might be a super type of another and they belong to
the class of types, so at the beginning of the method, there is an extra
procedure where the types from the same class will be reset to the one that is
mostly specific. E.g. if type A is a super type of B, (and note A and B belong
to the same class of type, but type A and type B could be different types
estimated by different method), then A is reset to B, or vise-versa. Then for
each type we compute the posterior probability with conditions on the types
detected by each method.
By default, the probability selection mechanism is disabled, in order to enable
it, set "useProbSelection:: boolean" to true (the default value is false);
To be continued
> MIME type detection with probability
> ------------------------------------
>
> Key: TIKA-1517
> URL: https://issues.apache.org/jira/browse/TIKA-1517
> Project: Tika
> Issue Type: Improvement
> Components: mime
> Affects Versions: 0.1-incubating, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9,
> 0.10, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6
> Reporter: Luke sh
> Attachments: BaysianTest.java
>
>
> Improvement and intuition
> The original implementation for MIME type selection/detection is a bit less
> flexible by initial design, as it heavily relies on the outcome produced by
> magic-bytes MIME Type identification; Thus e.g. if magic-bytes is applicable
> in a file, Tika will follow the file type detected by magic-bytes. It may be
> better to provide more control over the method of choice.
> This proposed approach slightly incorporate the Bayesian probability theorem,
> where users are able to assign weights to each approach in terms of
> probability, so they have the control over preference of which file type or
> mime type identification methods implemented/available in Tika, and currently
> there are 3 methods for identifying MIME type in Tika (i.e. Magic-Bytes, File
> extension and Metadata content-type hint). By introducing some weights on the
> approach in the proposed approach, users are able to choose which method they
> trust most, the magic-bytes method is often trust-worthy though. But the
> virtue is that in some situations, file type identification must be
> sensitive, some might want all of the MIME type identification methods to
> agree on the same file type before they start processing those files,
> incorrect file type identification is less intolerable. The current
> implementation seems to be less flexible for this purpose and heavily rely on
> the Magic-bytes file identification method (although magic-bytes is most
> reliable compared to the other 2 );
> Proposed design:
> The idea of selection is to incorporate probability as weights on each MIME
> type identification method currently being implemented in Tika (they are
> Magic bytes approach, file extension match and metadata content-type hint).
> for example,
> as an user, i would probably like to assign the the preference to the method
> based on the degree of the trust, and order the results if they don't
> coincide.
> Bayesian rule may be a bit appropriate here to meet the intuition.
> The following is what are needed for Bayesian rule implementation.
> > Prior probability P(file_type) e.g. P(pdf), theoretically this is computed
> > based on the samples, and this depends on the domain or use cases,
> > intuitively we more care the orders of the weights or probability of the
> > results rather than the actual numbers, and also the context of Prior
> > depends on samples for a particular use case or domain, e.g. if we happen
> > to crawl a website that contains mostly the pdf files, we probably can
> > collect some samples and compute the prior, based on the samples we can say
> > 90% of docs are pdf, so our prior is defined to be P(pdf) = 0.9, but here
> > we propose to define the prior as configurable param for users, and by
> > default we leave the prior to be "unapplicable". Alternatively, we can
> > define prior for each file type to be 1/[number of supported file types in
> > Tika] I think the number would be approximately 1/1157 and using this
> > number seems to be more fair, but the point of avoiding it is that this
> > prior is fixed for every type, and eventually we care more the orders of
> > the result and if the number is fixed, so will the order be, bringing this
> > number of 1/1157 into the Bayesian equation will not only be unable to
> > affect the order but also it will lumber our implementation with extra
> > computation, thus we will leave it as "unapplicable" which means we assign
> > 1 to it as it never exists! but note we care more the order rather the
> > actual number, and this param is configurable, and we believe it provides
> > much flexibilities in some use cases.
> > Conditional probability of positive tests given a file type P(test|
> > file_type) e.g. P(test1 = pdf | pdf), this probability is also based on
> > collection of samples and domain or use cases, we leave it configurable,
> > but based on our intuition we think test1(i.e. Magic-bytes method) is most
> > trustworthy, thus the default value is 0.75 for P(test1 = a_file_type |
> > a_file_type), this is to say given the file whose type is "a file type",
> > the probability of the test1 predicting the file is "a_file_type" is 0.75,
> > that is really our intuition, as we trust test1 most, next we propose to
> > use 0.7 for test3, and 0.65 for test2;
> (note again, test1 = magic-bytes, test2 = file extension, test3 = Metadata
> Content-type hint)
> > Conditional probability of negative tests also need to be intuitively
> > defined.
> E.g. By default, given a file type that is not pdf, the probability of test1
> predicting it is pdf is 1-P(test1 = pdf | pdf), thus P(test1=pdf | ~pdf) = 1-
> 0.75 = 0.25, as we trust the test1 the most, the other tests are defined with
> 0.35 and 0.3 respectively with the same intuition.
>
> >> The goal is to find out
> P(file_type | test1 = file_type, test2=file_type, test3=file_type)
> (Please note, we are mostly interested in the order of choice rather than the
> explicit computation, we selectively drop some of the parameters used in
> Bayesian rule. Those are not considered will by default be set to 1 .)
> For example, given a file the 3 tests have predicted as follows
> test1 = pdf
> test2 = pdf
> test3 = pdf
> prior: P(pdf) = 1 and P(~pdf) = 1 (meaning they are not applicable )
> P(test1=pdf|pdf) = 0.75
> P(test2=pdf|pdf) =0.65
> P(test3=pdf|pdf) = 0.7
> With the same concept or intuition, we have the negative conditional
> probability by default
> P(test1=pdf|~pdf) = 0.25
> P(test2=pdf|~pdf) =0.35
> P(test3=pdf|~pdf) = 0.3
> Then we ready to compute.
> Our goal is P(pdf|test1=pdf, test2=pdf, test3=pdf)
> P(pdf|test1=pdf, test2=pdf, test3=pdf) = [P(pdf) * P(test1=pdf|pdf) *
> P(test2=pdf|pdf) * P(test3=pdf|pdf)]/total probability
> where
> total probability = P(pdf) * P(test1=pdf|pdf) * P(test2=pdf|pdf) *
> P(test3=pdf|pdf) + P(~pdf) P(test1=pdf|~pdf) * P(test2=pdf|~pdf) *
> P(test3=pdf|~pdf) = 0.3675
> Thus,
> P(pdf|test1=pdf, test2=pdf, test3=pdf) = 0.92857
> ---------------------------------------------------------------------------------
> example 2
> test1=pdf
> test2=txt
> test3=txt
> In this example, test2 and test3 does not agree test1.
> So we have 2 types to compare, let's compute the 2 file type probabilities
> with conditions on those test results.
> for simplicity,
> test1=pdf, i will write test1+
> > pdf
> P(\+|test1+, test2-, test3-)
> = [P(+)P(test1+|pdf)*P(test2-|pdf)*P(test3-|pdf)]/total probability
> = 0.40909
> >text
> P(\+|test1-, test2+, test3+) = 0.590909
> ---------------------------------------------------------------------------------
> example 3
> test1=pdf
> test2=txt
> test3=uc
> > pdf
> P(\+|test1+, test2-, test3-) = 0.40909
> >txt
> P(\+|test1-, test2+, test3-) = 0.20968
> >nc
> P(\+|test1-, test2-, test3+) = 0.29518
> ---------------------------------------------------------------------------------
> Since we are more interested in the weight order in a way we prefer to put
> more weight on the methods with higher preference, we can further simplify
> computation by ignoring the probability of the tests that have negative
> prediction.
> Consider the example 3 above,
> > pdf
> P(\+|test1+, test2-, test3-) = 0.40909
> we can ignore probability of test2- and test3-, as we are more interested in
> the order of preference;
> the equation can be rewriten as follows
> P(\+|test1+) = 0.75/(0.75+0.35) = 0.75 where the total probability becomes 1,
> note prior is set to 1 by default for simplicity too.
> Similarly, we have
> >txt
> P(\+|test2+) = 0.65
> >uc
> P(\+|test3+)=0.7
> This follows the initial intuitive assumption and the intuitive order is also
> preserved.
> some of the parameters are being left out for computation simplicity by
> default, but the goal is to provide a way thru which users are able to
> control which method they want to use with probability weights, and this also
> provides some rooms or flexibility for more MIME detection algorithms.
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