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ENH Add drop_intermediate parameter to metrics.precision_recall_curve #24668

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Merged
merged 19 commits into from
Mar 24, 2023

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dberenbaum
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Reference Issues/PRs

Fixes #21825

What does this implement/fix? Explain your changes.

Adds a drop_intermediate kwarg to metrics.precision_recall_curve similar to the one that already exists for metrics.roc_curve. This removes unnecessary points on the curve to reduce its size.

# with the same tps value have the same recall and thus x coordinate.
# They appear as a vertical line on the plot.
optimal_idxs = np.where(
np.r_[True, np.logical_or(np.diff(tps[:-1]), np.diff(tps[1:])), True]
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For my education: why does taking the "second derivative" in roc_curve work, but here it doesn't?

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More precisely, why does using the second derivative work for roc_curve? What we are looking for is two (or more) points where there is no change, so the first derivative seems like the natural thing to use :-/

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Thinking about this some more, could we use np.r_[True, np.diff(tps, 2), True] instead?

For tps = [1, 2, 3, 3, 3, 5, 6] we'd get [1, 3, 3, 5, 6] (the two gets dropped because its is on the line between 1 and 3. For tps = [1,2.1,3,3,3,5,6] we get [1., 2.1, 3., 3., 5., 6.].

I guess for plotting purposes it is fine to remove the 2?! Is there a reason to have different behaviour regarding the removal of points in roc_curve and this (with np.logical_or(np.diff(tps[:-1]), np.diff(tps[1:])) the 2 is kept)?

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@dberenbaum dberenbaum Oct 17, 2022

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The difference is that both axes of an ROC curve have constant denominators:

  • fpr = fps / fps[-1] (linearly correlated with fps)
  • tpr = tps / tps[-1] (linearly correlated with tps)

By contrast, precision has a non-constant denominator (note that recall = tpr):

  • precision = tps / (tps + fps) (not linearly correlated with either tps or fps)

If you extend your example by one to tps = [1, 2, 3, 3, 3, 5, 6, 7], then you will get:

tps = [1, 3, 3, 3, 5, 6, 7]
fps = [0, 0, 1, 2, 2, 2, 2]
tpr = [1/7, 3/7, 3/7, 3/7, 5/7, 6/7, 7/7]
fpr = [0/2, 0/2, 1/2, 2/2, 2/2, 2/2, 2/2]
precision = [1/1, 3/3, 3/4, 3/5, 5/7, 6/8, 7/9]

np.r_[True, np.logical_or(np.diff(tps[:-1]), np.diff(tps[1:])), True] results in [1, 3, 3, 5, 6, 7].

np.r_[True, np.diff(tps, 2), True] results in [1, 3, 3, 5, 7].

The second method incorrectly drops the 6, which is not actually on a line in the precision-recall curve:

image

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Today I learnt! Thanks for taking the time to explain it

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Looks good to me.

Does this need an entry in "what's new"?

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Looks good to me.

Does this need an entry in "what's new"?

Not sure if this question is to me? I'm not sure what justifies a "what's new" entry but happy to provide one if needed.

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betatim commented Oct 18, 2022

Not sure if this question is to me? I'm not sure what justifies a "what's new" entry but happy to provide one if needed.

It was aimed at someone "in the know", because I also don't know the inclusion criteria.

@glemaitre glemaitre changed the title [MRG] Add drop_intermediate kwarg to metrics.precision_recall_curve ENH Add drop_intermediate parameter to metrics.precision_recall_curve Nov 3, 2022
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We will need an entry in the changelog doc/whats_new/v1.2.rst, in the appropriate section. We can consider this as an enhancement. We should mentioned that the parameter is added to both the precision_recall_curve and the associated display.

dberenbaum and others added 8 commits November 3, 2022 19:46
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
…parameter to `metrics.precision_recall_curve`
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Thanks @glemaitre! The comments should be addressed.

@glemaitre glemaitre self-requested a review November 4, 2022 19:09
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I pushed 2 fixes for the CIs to pass.
However, I still think that we need to force drop_intermediate=False for the moment before making a deprecation cycle.

dberenbaum and others added 4 commits November 4, 2022 17:04
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
@dberenbaum
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However, I still think that we need to force drop_intermediate=False for the moment before making a deprecation cycle.

Sorry, just an oversight that I missed fixing in the rest of the code. 🙏

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
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LGTM is on my side. Thanks @dberenbaum.

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LGTM. Maybe we can get this merged in time for v1.2

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Hi @betatim, just checking if there's anything I can do to help get this merged?

@glemaitre glemaitre added the Waiting for Second Reviewer First reviewer is done, need a second one! label Jan 9, 2023
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Adding the Waiting for second reviewer flag.

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betatim commented Jan 11, 2023

Hi @betatim, just checking if there's anything I can do to help get this merged?

In general no, we need a second reviewer to come along and review this. Having the label should help with that.

However, since v1.2 has been released already the changelog entry needs to move from the v1.2 file to the v1.3 file. Sorry for that busy work.

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Thanks for the PR @dberenbaum. I see that there are already 2 approvals, I just updated the target version to 1.3. Let's merge.

@jeremiedbb jeremiedbb merged commit 01e1f97 into scikit-learn:main Mar 24, 2023
Veghit pushed a commit to Veghit/scikit-learn that referenced this pull request Apr 15, 2023
MohitBurkule added a commit to MohitBurkule/scikit-learn that referenced this pull request May 7, 2023
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* MAINT Parameters validation for sklearn.metrics.pairwise.rbf_kernel (scikit-learn#26071)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.metrics.pairwise.sigmoid_kernel (scikit-learn#26072)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Param validation: constraint for numeric missing values (scikit-learn#26085)

* FIX Adds support for negative values in categorical features in gradient boosting (scikit-learn#25629)

Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>
Co-authored-by: Tim Head <betatim@gmail.com>

* MAINT Fix C warning in Cython module splitting.pyx (scikit-learn#26051)

* MNT Updates _isotonic.pyx to use memoryviews instead of `cnp.ndarray` (scikit-learn#26068)

* FIX Fixes memory regression for inspecting extension arrays (scikit-learn#26106)

* PERF set openmp to use only physical cores by default (scikit-learn#26082)

* MNT Update black to 23.3.0 (scikit-learn#26110)

* MNT Adds black commit to git-blame-ignore-revs (scikit-learn#26111)

* MAINT Parameters validation for sklearn.metrics.pair_confusion_matrix (scikit-learn#26107)

* MAINT Parameters validation for sklearn.metrics.mean_poisson_deviance (scikit-learn#26104)

* DOC Use notebook style in plot_lof_outlier_detection.py (scikit-learn#26017)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

* MAINT utils._fast_dict uses types from utils._typedefs (scikit-learn#26025)

* DOC remove sparse-matrix for `y` in ElasticNet (scikit-learn#26127)

* ENH add exponential loss (scikit-learn#25965)

* MAINT Parameters validation for sklearn.preprocessing.robust_scale (scikit-learn#26086)

* MAINT Parameters validation for sklearn.datasets.fetch_rcv1 (scikit-learn#26126)

* MAINT Parameters validation for sklearn.metrics.adjusted_rand_score (scikit-learn#26134)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.metrics.calinski_harabasz_score  (scikit-learn#26135)

* MAINT Parameters validation for sklearn.metrics.davies_bouldin_score  (scikit-learn#26136)

* MAINT: remove `from numpy.math cimport` statements (scikit-learn#26143)

* MAINT Parameters validation for sklearn.inspection.permutation_importance (scikit-learn#26145)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.metrics.cluster.homogeneity_completeness_v_measure (scikit-learn#26137)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.metrics.rand_score (scikit-learn#26138)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* DOC update comment in metrics/tests/test_classification.py (scikit-learn#26150)

* CI small cleanup of Cirrus CI test script (scikit-learn#26168)

* MAINT remove deprecated is_categorical_dtype (scikit-learn#26156)

* DOC Add skforecast to related projects page (scikit-learn#26133)

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

* FIX Keeps namedtuple's class when transform returns a tuple (scikit-learn#26121)

* DOC corrected letter case for better readability in sklearn/metrics/_classification.py / (scikit-learn#26169)

* MAINT Parameters validation for sklearn.preprocessing.power_transform (scikit-learn#26142)

* FIX `roc_auc_score` now uses `y_prob` instead of `y_pred` (scikit-learn#26155)

* MAINT Parameters validation for sklearn.datasets.load_iris (scikit-learn#26177)

* MAINT Parameters validation for sklearn.datasets.load_diabetes (scikit-learn#26166)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.datasets.load_breast_cancer (scikit-learn#26165)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.metrics.cluster.entropy (scikit-learn#26162)

* MAINT Parameters validation for sklearn.datasets.fetch_species_distributions (scikit-learn#26161)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* ASV Fix tol in SGDRegressorBenchmark (scikit-learn#26146)

Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr>

* MNT use api.openml.org URLs for fetch_openml (scikit-learn#26171)

* MAINT Parameters validation for sklearn.utils.resample (scikit-learn#26139)

* MAINT make it explicit that additive_chi2_kernel does not accept sparse matrix (scikit-learn#26178)

* MNT fix circleci link in README.rst (scikit-learn#26183)

* CI Fix circleci artifact redirector action (scikit-learn#26181)

* GOV introduce rights for groups as discussed in SLEP019 (scikit-learn#25753)

Co-authored-by: Julien <git@jjerphan.xyz>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

* MAINT Parameters validation for sklearn.neighbors.sort_graph_by_row_values (scikit-learn#26173)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* FIX improve convergence criterion for LogisticRegression(penalty="l1", solver='liblinear') (scikit-learn#25214)

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

* MAINT Fix several typos in src and doc files (scikit-learn#26187)

* PERF fix overhead of _rescale_data in LinearRegression (scikit-learn#26207)

* ENH add Huber loss (scikit-learn#25966)

* MAINT Refactor GraphicalLasso and graphical_lasso (scikit-learn#26033)

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Cython linting (scikit-learn#25861)

* DOC Add JupyterLite button in example gallery (scikit-learn#25887)

* MAINT Parameters validation for sklearn.covariance.ledoit_wolf_shrinkage (scikit-learn#26200)

* MAINT Parameters validation for sklearn.datasets.load_linnerud (scikit-learn#26199)

* MAINT Parameters validation for sklearn.datasets.load_wine (scikit-learn#26196)

* DOC Added redirect to Provost paper + minor refactor (scikit-learn#26223)

* MAINT Parameter Validation for `covariance.graphical_lasso` (scikit-learn#25053)

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.datasets.load_digits (scikit-learn#26195)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.preprocessing.quantile_transform (scikit-learn#26144)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.model_selection.cross_validate (scikit-learn#26129)

Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr>

* DOC Adds TargetEncoder example explaining the internal CV (scikit-learn#26185)

Co-authored-by: Tim Head <betatim@gmail.com>

* spelling mistake corrected in documentation for script `plot_document_clustering.py` (scikit-learn#26228)

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

* FIX possible UnboundLocalError in fetch_openml (scikit-learn#26236)

* ENH Adds PyTorch support to LinearDiscriminantAnalysis (scikit-learn#25956)

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
Co-authored-by: Tim Head <betatim@gmail.com>

* MNT Use fixed version of Pyodide (scikit-learn#26247)

* MNT Reset transform_output default in example to fix doc build build (scikit-learn#26269)

* DOC Update example plot_nearest_centroid.py (scikit-learn#26263)

* MNT reduce JupyterLite build size (scikit-learn#26246)

* DOC term -> meth in GradientBoosting (scikit-learn#26225)

* MNT speed-up html-noplot build (scikit-learn#26245)

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

* MNT Use copy=False when creating DataFrames (scikit-learn#26272)

* MAINT Parameters validation for sklearn.model_selection.permutation_test_score (scikit-learn#26230)

* MAINT Parameters validation for sklearn.datasets.clear_data_home (scikit-learn#26259)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.datasets.load_files (scikit-learn#26203)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.datasets.get_data_home (scikit-learn#26260)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* DOC Fix y-axis plot labels in permutation test score example (scikit-learn#26240)

* MAINT cython-lint ignores asv_benchmarks (scikit-learn#26282)

* MAINT Parameter validation for metrics.cluster._supervised (scikit-learn#26258)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* DOC Improve docstring for tol in SequentialFeatureSelector (scikit-learn#26271)

* MAINT Parameters validation for  sklearn.datasets.load_sample_image (scikit-learn#26226)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* DOC Consistent param type for pos_label (scikit-learn#26237)

* DOC Minor grammar fix to imputation docs (scikit-learn#26283)

* MAINT Parameters validation for sklearn.calibration.calibration_curve (scikit-learn#26198)

Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr>

* MAINT Parameters validation for sklearn.inspection.partial_dependence (scikit-learn#26209)

Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr>

* MAINT Parameters validation for sklearn.model_selection.validation_curve (scikit-learn#26229)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.model_selection.learning_curve (scikit-learn#26227)

Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr>

* MNT Remove deprecated pandas.api.types.is_sparse (scikit-learn#26287)

* CI Use Trusted Publishers for uploading wheels to PyPI (scikit-learn#26249)

* MAINT Parameters validation for sklearn.metrics.pairwise.manhattan_distances (scikit-learn#26122)

* PERF revert openmp use in csr_row_norms (scikit-learn#26275)

* MAINT Parameters validation for metrics.check_scoring (scikit-learn#26041)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MNT Improve error message when checking classification target is of a non-regression type (scikit-learn#26281)

Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

* DOC fix link to User Guide encoder_infrequent_categories (scikit-learn#26309)

* MNT remove unused args in _predict_regression_tree_inplace_fast_dense (scikit-learn#26314)

* ENH Adds missing value support for trees (scikit-learn#23595)

Co-authored-by: Tim Head <betatim@gmail.com>
Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>

* CLN Clean up logic in validate_data and cast_to_ndarray (scikit-learn#26300)

* MAINT refactor scorer using _get_response_values (scikit-learn#26037)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>
Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com>

* DOC Add HGBDT to "see also" section of random forests (scikit-learn#26319)

Co-authored-by: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Co-authored-by: Tim Head <betatim@gmail.com>

* MNT Bump Github Action labeler version to use newer Node (scikit-learn#26302)

* FIX thresholds should not exceed 1.0 with probabilities in `roc_curve`  (scikit-learn#26194)

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

* ENH Allow for appropriate dtype us in `preprocessing.PolynomialFeatures` for sparse matrices (scikit-learn#23731)

Co-authored-by: Aleksandr Kokhaniukov <alexander.kohanyukov@gmail.com>
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

* DOC Fix minor typo (scikit-learn#26327)

* MAINT bump minimum version for pytest (scikit-learn#26184)

Co-authored-by: Loïc Estève <loic.esteve@ymail.com>
Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com>
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

* DOC fix return type in isotonic_regression (scikit-learn#26332)

* FIX fix available_if for MultiOutputRegressor.partial_fit (scikit-learn#26333)

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

* FIX make pipeline pass check_estimator (scikit-learn#26325)

* FEA Add multiclass support to `average_precision_score` (scikit-learn#24769)

Co-authored-by: Geoffrey <geoffrey.bolmier@gmail.com>
Co-authored-by: gbolmier <geoffrey.bolmier@volvocars.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

---------

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Add drop_intermediate to precision_recall_curve
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