Fix predict method for multiclass multioutput ensemble models#12834
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jnothman merged 4 commits intoscikit-learn:masterfrom Jan 2, 2019
Merged
Fix predict method for multiclass multioutput ensemble models#12834jnothman merged 4 commits intoscikit-learn:masterfrom
jnothman merged 4 commits intoscikit-learn:masterfrom
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adrinjalali
reviewed
Dec 20, 2018
sklearn/ensemble/forest.py
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| n_samples = proba[0].shape[0] | ||
| predictions = np.zeros((n_samples, self.n_outputs_)) | ||
| predictions = np.empty((n_samples, self.n_outputs_), | ||
| dtype='object') |
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wouldn't it be better to have dtype=self.classes_.dtype or something?
jnothman
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Dec 20, 2018
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| with np.errstate(divide="ignore"): | ||
| proba = est.predict_proba(X_test) | ||
| assert_equal(len(proba), 2) |
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With the adoption of pytest, we are phasing out use of test helpers assert_equal, assert_true, etc. Please use bare assert statements, e.g. assert x == y, assert not x, etc.
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Sorry for the delay! I committed a couple of changes to address the code review comments. |
jnothman
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Jan 2, 2019
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LGTM!
Please add an entry to the change log at doc/whats_new/v0.21.rst. Like the other entries there, please reference this pull request with :issue: and credit yourself (and other contributors if applicable) with :user:
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Reference Issues/PRs
Fixes #12831.
What does this implement/fix? Explain your changes.
This PR fixes a bug where the
predictmethod would fail for multiclass multioutput ensemble models, if any of the dependent variables were strings. The underlying issue was preallocating thepredictoutput usingnp.zeros, which would then error when string predictions were inserted. I replaced the function call with a more dtype-agnostic call tonp.empty.Any other comments?