New classifiers #147
New classifiers #147
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| Also adds the classifiers's predictions as a 'SyntheticFeature' column. | ||
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| """ | ||
| return self._train_model_and_predict(input_df, AdaBoostClassifier, |
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learning_rate should be capped at > 0.
Looking at the docs further, I also think that for the AdaBoostClassifier, we should allow n_estimators to be evolved as well, with a max of 500 estimators. The AdaBoostClassifier is one unique case where there is a tradeoff between n_estimators and learning_rate.
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Should I just do
max(learning_rate, 0.001)Not sure what the exact minimum should be here.
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Check xgradient_boosting for an example: https://github.com/rhiever/tpot/blob/master/tpot/tpot.py#L582
learning_rate = max(0.0001, learning_rate)And for the C param, check _logistic_regression for an example: https://github.com/rhiever/tpot/blob/master/tpot/tpot.py#L516
C = max(0.0001, C)0.0001 seems like a fine minimum value for now until we finish the sklearn benchmark and figure out an ideal range.
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When writing test cases for classifiers, test with normal parameters as well as extreme parameters: negative values, out-of-bounds values, etc. That will help catch issues where we're allowing invalid parameters to be passed to the various models. |
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Now addresses #151 |
What does this PR do?
Adds 7 new classifiers to TPOT
Where should the reviewer start?
At the _ada_boost() method in the TPOT class, going all the way down to the _p_aggr() method.
There is also relevant export code and docs that should be checked for an LGTM.
How should this PR be tested?
Travis should test the classifiers themselves, but a few pipelines could be made and exported to confirm that the code indeed works.
What are the relevant issues?
#128
Questions:
Yes. I have already updated them.
No.