[MRG+1] Fix multi-label issues in IsolationForest benchmark #8638
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Thanks! I agree that the described fix is appropriate, but now that there are other changes you will need to be patient for a full review. I hope to look soon. |
TomDLT
approved these changes
Mar 31, 2017
benchmarks/bench_isolation_forest.py
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| ax[2].hist(scoring[y_test == 1], bins, color='r', | ||
| label='outliers') | ||
| ax[2].legend(loc="lower right") | ||
| y_pred = model.predict(X_test) |
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you don't need to call predict before decision_function, y_pred is unused,
and it artificially increases the predict time.
TomDLT
reviewed
Mar 31, 2017
benchmarks/bench_isolation_forest.py
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| bins = np.linspace(-0.5, 0.5, 200) | ||
| ax[0].hist(scoring, bins, color='black') | ||
| ax[0].set_title('Decision function for %s dataset' % dat) | ||
| ax[0].legend(loc="lower right") |
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You can remove this line, since there is no label in this plot.
It will remove the matplotlib warning
glemaitre
reviewed
Apr 3, 2017
benchmarks/bench_isolation_forest.py
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| ========================================== | ||
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| A test of IsolationForest on classical anomaly detection datasets. | ||
| """ |
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Could you put the description on the top of the file
glemaitre
reviewed
Apr 3, 2017
benchmarks/bench_isolation_forest.py
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| A test of IsolationForest on classical anomaly detection datasets. | ||
| """ | ||
| print(__doc__) |
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You can put the print(__doc__) just below the import and before print_outlier_ratio(y)
jmschrei
reviewed
Apr 3, 2017
benchmarks/bench_isolation_forest.py
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| fig_roc, ax_roc = plt.subplots(1, 1, figsize=(8, 5)) | ||
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| # Set this to true for plotting score histograms for each dataset: | ||
| with_scoring_hists = False |
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To be consistent with the previous example, shouldn't this be set to True by default?
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I found it clearer with just the ROC curves, hence the False by default (but can be changed if required).
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Could you please rename it "with_decision_functions_histograms"?
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LGTM |
…ges (mostly esthethic) Minor corrections after code review. Minor corrections after 2nd code review. Minor modif
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Thanks @hrjn ! |
Sundrique
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Jun 14, 2017
…earn#8638) * Fixed a legacy multi-label issue and added minor refactoring and changes (mostly esthethic) Minor corrections after code review. Minor corrections after 2nd code review. Minor modif * rerun CI
dmohns
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Aug 7, 2017
…earn#8638) * Fixed a legacy multi-label issue and added minor refactoring and changes (mostly esthethic) Minor corrections after code review. Minor corrections after 2nd code review. Minor modif * rerun CI
dmohns
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Aug 7, 2017
…earn#8638) * Fixed a legacy multi-label issue and added minor refactoring and changes (mostly esthethic) Minor corrections after code review. Minor corrections after 2nd code review. Minor modif * rerun CI
NelleV
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Aug 11, 2017
…earn#8638) * Fixed a legacy multi-label issue and added minor refactoring and changes (mostly esthethic) Minor corrections after code review. Minor corrections after 2nd code review. Minor modif * rerun CI
paulha
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Aug 19, 2017
…earn#8638) * Fixed a legacy multi-label issue and added minor refactoring and changes (mostly esthethic) Minor corrections after code review. Minor corrections after 2nd code review. Minor modif * rerun CI
maskani-moh
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Nov 15, 2017
…earn#8638) * Fixed a legacy multi-label issue and added minor refactoring and changes (mostly esthethic) Minor corrections after code review. Minor corrections after 2nd code review. Minor modif * rerun CI
jwjohnson314
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Dec 18, 2017
…earn#8638) * Fixed a legacy multi-label issue and added minor refactoring and changes (mostly esthethic) Minor corrections after code review. Minor corrections after 2nd code review. Minor modif * rerun CI
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Reference Issue
Fixes #8637
What does this implement/fix? Explain your changes.
In previous version, using Python 3 and
LabelBinarizerto encode categorical features from SA & SF datasets fromkddcup99led to the error described in #8637. Replacing it withMultipleLabelBinarizerfixes the problem and allows the code to run on both Python 2 and 3.Output obtained with new version:
Any other comments?
Additional minor cleaning/refactoring:
with_scoring_histsflag (set toFalseby default) to avoid plotting all score histograms (might potentially clog the screen with figure windows)