Comments (3)
Yeah I was actually thinking about this myself. Do you think it would work if chantilly checks that the model has a predict_proba_one
method? For instance with hasattr(model, 'predict_proba_one')
.
If we do this, there should also be some checks in the /api/model
POST route to check that the provided model has a fit_one
and either a predict_one
or a predict_proba_one
method.
Tell me what you think.
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Okay I just made the change. Now you provide any model that implements fit_one
and either predict_proba_one
or predict_one
. In other words you don't have to inherit from a creme
base class anymore. Sending a POST request to @/api/model
will raise an error if the model doesn't implement said methods.
I added some unit tests so it should be working fine. Tell me how it goes.
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@kevinsteger I added a new "flavor" concept. Basically the flavor is in charge of validating the provided models. Again it doesn't require to provide a creme model, only a class that implements the required methods. You can see the associated documentation here. I'll let you close this if you're happy with it.
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Related Issues (20)
- Consider cloudpickle
- Consider Blinker
- Consider making /api/predict a GET operation instead of POST HOT 3
- 500 error posting ClassifierChain model HOT 3
- Display model memory usage
- Integrate with AWS
- Unable to upload creme model HOT 1
- invalid javascript syntax in example
- trouble using loaded model
- Question on fit_one function HOT 2
- Unable to run in Windows HOT 5
- 'Unsupported operand type' error prediction in README example HOT 1
- Update metrics - possibly missing a case? HOT 20
- Can this be used for un-labeled models as well? And how many?
- Websocket implementation
- Allow flavors HOT 5
- Write performance benchmark HOT 7
- move default error log location HOT 2
- Error when POST a new model. HOT 4
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