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Add wrapper for FactorizationMachiones algorithm. - #38
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Require default_mini_batch_size during object creation for algorithms that require mini_batch_size. Use default_mini_batch_size instead of hardcoded value.
Update documentation in the class. Update integration tests for modifications.
Bump up the version.
owen-t
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FM implementation is good - a few concerns about the default / required argument changes.
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Love this! Should have done this from the get-go.
| predictor_type = hp('predictor_type', isin('binary_classifier', 'regressor'), | ||
| 'Value "binary_classifier" or "regressor"') | ||
| epochs = hp('epochs', (gt(0), isint), "An integer greater than 0") | ||
| clip_gradient = hp('clip_gradient', isfloat, "A float value") |
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This forces an explicit assignment, where an implicit assignment would be fine.
If I have a FacotrizationMachines object fm, then:
fm.clip_gradient = 55
will fail, because 55 is not float - even though it can be represented as a float. Consider using the isnumber check instead.
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That's a good idea - will change
| def __init__(self, role, train_instance_count, train_instance_type, k, init_method=None, | ||
| max_iterations=None, tol=None, num_trials=None, local_init_method=None, | ||
| def __init__(self, role, train_instance_count, train_instance_type, k, default_mini_batch_size=5000, |
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Introducing another argument with default value is a little dangerous. If someone is calling this function will arguments set positionally, then this will fail.
That said, I think we can take this risk - the chance of this problem occuring is low and we're doing a new release. I'd recommend including constructor signature changes in the changelog.
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This is a required parameter and must be specified by the user. An alternative to constructor declaration will be fit enforcement. Let me rewrite it in that alternative form.
| num_point_for_scalar = hp('num_point_for_scalar', (isint, gt(0)), 'An integer greater-than 0') | ||
| def __init__(self, role, train_instance_count, train_instance_type, predictor_type='binary_classifier', | ||
| def __init__(self, role, train_instance_count, train_instance_type, predictor_type, |
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Why move this to a required argument? This will break clients working with the default.
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This is a required argument and we shouldn't default.
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Since it is a minor release change we will not break existing calls and I'll leave it then.
| validation_message="Value must be an integer greater than or equal to 0") | ||
| def __init__(self, role, train_instance_count, train_instance_type, num_components, | ||
| def __init__(self, role, train_instance_count, train_instance_type, num_components, default_mini_batch_size, |
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I'd like to try to avoid including new required arguments to function signatures. Why can't this have a default value?
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This is a required parameter that has no default. Instead of constructor let's enforce by fit() in new algorithms and I will leave default logic here to avoid breaking existing calls.
| isint = istype(int) | ||
| isbool = istype(bool) | ||
| isnumber = istype(numbers.Number) # noqa | ||
| isfloat = istype(float) |
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As discussed above, probably don't need this.
XGBoost notebook changes
* Add FactorizationMachines class with unit tests. * Add integration test for FactorizationMachines. * Add CHANGELOG file.
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