Hello Martin, All,


Martin Guetlein wrote:
> Hello All,
>
> On Fri, Dec 4, 2009 at 3:09 PM, Christoph Helma <helma at in-silico.de> wrote:
>   
>> Excerpts from Nina Jeliazkova's message of Fri Dec 04 12:39:56 +0100 2009:
>>     
>>>> I use the following workflow:
>>>>
>>>> POST /descriptor_calculation training_dataset                                    # creates feature_dataset
>>>> POST /algorithm              training_dataset feature_dataset # creates model
>>>> POST /model                  compound_uri                     # creates prediction
>>>> or
>>>> POST /model                  prediction_dataset               # creates dataset with predictions
>>>>
>>>> This is fairly straightforward and allows you to reuse/exchange descriptors.
>>>>
>>>>         
>>> Yes, but straightforward implementation duplicates information
>>> (training/feature datasets are not very much different).
>>>       
>> No, training and feature datasets are disjunct in my case. This allows
>> me e.g. to quickly create lazar models with different types of
>> descriptors and compare the results with other algorithms.
>>     
>
>
> To determine the parameters for building a prediction model (what to
> predict?, which features to use?) is needed for the validation as
> well.
> I made a proposal how the curl call for validating an algorithm could
> look like (see http://www.opentox.org/data/documents/development/validation/validation-and-reporting-overview-and-data-flow).
> An excerpt:
>
>   
Could you tell, why one would validate an "algorithm", and not a "model"
? (sorry if already discussed, I am a bit confused).

Otherwise, it seems the current API for algorithms
http://opentox.org/dev/apis/api-1.1/Algorithm is a bit underspecified  :

    "parameters are algorithm dependent, specified by service provider
    in the algorithm representation".


Perhaps it make sense to introduce similar parameter/parameter names for
the algorithm service itself, then the validation service could use the
algorithm service calls?

Best regards,
Nina
>   curl -X POST -d algorithm_uri="<algorithm_service>/algorithm/<algorithm_id>" \
>                -d
> training_dataset_uri="<dataset_service>/dataset/<train_dataset_id>" \
>                -d
> test_dataset_uri="<dataset_service>/dataset/<test_dataset_id>" \
>                -d prediction_feature="<prediction_feature>" \
>                -d
> algorithm_params="<alg_param_key_1>=<alg_param_val1>;<alg_param_key_2>=<alg_param_val2>"
> [OPTIONAL]\
>                <validation_service>/validation
>
> -> validation-internal api call to build model:
>
>   curl -X POST -d dataset_uri="<dataset_service>/dataset/<train_dataset_id>" \
>                -d prediction_feature="<prediction_feature>" \
>                -d <alg_param_key1>="<alg_param_val1>" \
>                -d <alg_param_key2>="<alg_param_val2>" \
>                 <algorithm_service>/algorithm/<algorithm_id>
>
> What do yout think?
>
> Best regards,
> Martin
>
>
>
>