DOC Add JupyterLite button in example gallery #25887
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Is this PR blocked by sphinx-gallery/sphinx-gallery#1100 and resolving the tests failures in https://github.com/lesteve/scikit-learn-tests-pyodide? I can see there is some ongoing work to get SciPy + OpenBLAS to work: pyodide/pyodide#3331 aiming to resolve some of the WASM tests failures in scikit-learn. |
I would hope not 😉. In my opinion, there are a few ways towards this PR getting merged even without the Scipy and scikit-learn tests passing on Pyodide (this could take a while to get them to pass if I am being honest):
Yes this too will take a while. There is some hope that it may fix some of the weird issues we see, but it is hard to tell without doing the (hard and long) work ... |
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To work around the CircleCI limitation, I made the output of a local run Here is one particular example, if you want to play with it: http://lesteve.github.io/scikit-learn/stable/auto_examples/release_highlights/plot_release_highlights_1_2_0.html |
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Thanks for the update. The "launch lite" button is giving a 404: |
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Indeed the bug is fixed in sphinx-gallery development version, the right URL is http://lesteve.github.io/scikit-learn/stable/lite/lab/?path=auto_examples/release_highlights/plot_release_highlights_1_2_0.ipynb |
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Having a "beta" label on the button would be a good start I think (easier than blacklisting examples). If it leads to a huge number of complaints (instead of constructive issues) it is easy enough to remove the button again from the docs. So I think we should do what Loic suggested and not wait for all things to be resolved. |
Once the dependency issue is resolved, I'm okay with the big warning option at the beginning of each notebook that only appears in JupyterLite. |
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So right now I have added warnings at the top of each notebook like this:
There is also a cell for JupyterLite-specific code that should cover most of the use-cases:
I put a red warning for the one notebook that uses Plotly because I think Plotly has issues in JupyterLite (although the notebook seems to be working for some reason) I think the main thing that would be needed to get this merged is a sphinx-gallery release. At the same time it is good to stress-test sphinx-gallery a bit more before doing a release ... What else do you think is needed to get this merged?
Reminder: you can look at an example here (CircleCI limitation prevents JupyterLite to work inside the CircleCI artifacts): |
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I just tried the linked example, running the cells, it says pandas is missing from the environment, and |
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Thank you for the update. This is looking good!
Is a beta near the Jupyter logo a must?
I think the banner in the notebook is enough.
open JupyterLite in a new tab
I think the current behavior is okay. The binder link does not open in a new tab either. (I'll prefer to open in another tab, but it's not a blocker.)
As for the example: lesteve.github.io/scikit-learn/stable/auto_examples/release_highlights/plot_release_highlights_1_2_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-2-0-py, I'm getting a BadStatusLine: HTTP/1.1 0 error running fetch_openml:
fetch_openml(
"titanic", version=1, as_frame=True, return_X_y=True, parser="pandas"
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Thanks a lot for your feed-back:
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doc/conf.py
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| 'parser="pandas"' in notebook_content_str | ||
| or "as_frame=True" in notebook_content_str | ||
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| code_lines.extend("import pandas") |
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Does JupyterLite require an "import pandas" before a library can use it?
In any case, I am okay with running "import pandas" all the time to prevent any pandas related issues.
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My understanding is that there is a bit of magic in Pyodide that if it you type import matplotlib in the Pyodide console (or in JupyterLite with a Pyodide kernel in our case) it will "just work". This is the case for all packages that are included inside Pyodide, including pandas.
On the other hand if you are calling a function that does an import inside it it will not work. Examples where this happens:
from sklearn.utils import check_matplotlib_support; check_matplotlib_support('test')%matplotlib inline
A work-around is to do the import before calling the function.
The kind of error you get:
ModuleNotFoundError: The module 'matplotlib' is included in the Pyodide distribution, but it is not installed.
You can install it by calling:
await micropip.install("matplotlib") in Python, or
await pyodide.loadPackage("matplotlib") in JavaScript
See https://pyodide.org/en/stable/usage/loading-packages.html for more details.
I am not sure how much hope there is too fix it in Pyodide. I would agree that this is indeed suprising.
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Wow, I see. In that case, can we work around these issues by always importing packages that is commonly use in the examples, such as matplotlib, pandas, etc.?
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I guess that would be another option indeed, I was trying to add some code only in the notebooks where it was needed but maybe this is not worth the effort ...
Also I opened pyodide/pyodide#3771 to get some feeling whether the underlying issue is fixable.
doc/conf.py
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| "from sklearn.datasets import _openml", | ||
| "_openml._OPENML_PREFIX = 'https://api.openml.org/'", |
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With #26171 merged, is this patch required?
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Yes for now until the OpenML header thing is fixed.
Note that the scikit-learn version we are using is the version available in Pyodide, namely 1.2.2 at the time of writing and #26171 only is in main.
I have some medium-term plan to add a CI build for the scikit-learn development version, which potentially could be published similarly to our scikit-learn development wheel and installed in JupyterLite but it will take a bit of time.
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To check my understanding: if there was a Pyodide build that includes #26171, can this OpenML work-around be removed without waiting for openml/OpenML#1135 to be resolved?
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Yes, although my favourite way out would be that the OpenML CORS issue can be fixed in the not too distant future and this temporary work-around can be removed.
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I think this is in a good enough state to be reviewed, I have updated https://lesteve.github.io/scikit-learn, the 1.2 release highlight example runs fine. Feel free to run other examples from the gallery and find other issues 😉! |
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I tried a few examples, they all ran nicely, except that for some of them they'd get stuck in a cell (not necessarily the import one), and after a kernel restart, they run fine. This is NICE! |
Interesting, if you remember the particular notebooks that get stuck, I can have a closer look. Now that using OpenBLAS pyodide/pyodide#3331 has been merged, the full scikit-learn pass (except a few xfailed tests) with the Pyodide development version see https://github.com/lesteve/scikit-learn-tests-pyodide for more details, so I am hoping this kind of intermittent issues will be a thing of the past.
Thanks 😉! |
It's not something I can reproduce, so I'm happy to have this merged and have a broader testing audience and take it from there. |
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I'm trying to see how this alters our release process. At a high level, I see this process:
Is this correct? If so, my concern is the wait for Pyodide to release. There will be a time period where our examples were updated to v1.3, but not run on the version packaged by Pyodide. |
Would there be a way for the examples to specify exact version of scikit-learn they want installed, and fail if it's not released, with a nice message that users need to wait for that, and in the meantime they can download the notebooks for themselves or use mybinder? |
Good point indeed! I can envision a future where we build a Pyodide wheel in our CI and we upload it somewhere (TBD) and then we could add Full disclosure, I have not done it so I may be oversimplifying the situation ... it seems like pydantic-core has wasm wheels in its github release https://github.com/pydantic/pydantic-core/releases/tag/v0.25.0 but for now PyPI does not support wasm wheels e.g. https://discuss.python.org/t/support-wasm-wheels-on-pypi/21924. cc @rth in case I am oversimplifying the situation too much on the Pyodide wheel packaging and installing part. Before this future happens, the process you mention seems to describe accurately what would happen. There is an additional thing that I am not sure about, is how JupyterLite controls the Pyodide version. Right now it seems like JupyterLite has Pyodide 0.23.0 although Pyodide 0.23.1 has been released a few days ago.
I guess we could add a cell that checks the version at the beginning with a nice error message if the scikit-learn version is not recent enough. You would still need to figure out (and maintain) which version is needed by each example but maybe this is worth it. |
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I'm a bit confused, can we not have a specific version installed in that environment? Let's say two releases down the line, what happens if the user looks at an example from an older version, and clicks on the button, do they get an environment with the most recent version instead of the one corresponding to the example? |
Right now, JupyterLite has a fixed Pyodide version, and Pyodide has a fixed version of scikit-learn (1.2.2 for Pyodide 0.23.0 for example). As I was trying to say we could build ourselves a scikit-learn wasm wheel, make it accessible somewhere so that it can be installed in the notebook via
I am not too sure how that would work, but at first sight it seems non trivial to support the older version use case with JupyterLite. I would guess this use case is very rarely used. In an ideal world I agree we would support at the very least stable vs dev though. Checking the Binder link behavior, you actually get a scikit-learn development version, even from the stable doc (and even from older versions). Nobody seems to have complained about it yet 😉. |
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Is it feasible to build pyodide (or JupyterLite? or both?) with the specific version of scikit-learn that the docs were built with? Then we could ship everything needed, new versions would work and old ones would keep working as well because everything is hosted in the build output of the docs page. |
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I guess my preference would be for this PR to be merged in its current state and then to iterate in further PRs to make it more robust. I feel like merging this PR would be useful to gather wider feed-back and it is unlikely to be too disruptive since it is only on the dev website. I am quite hopeful that there is a way forward to make it more robust and that it will work by the time the next scikit-learn release comes around. I would totally understand if cautiousness is preferred though and that the consensus is making it more robust before merging this PR. Right now I think the best way forward looks like this:
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Great thanks a lot! |
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This is a really great achievement. Thanks so much to anyone involved and @lesteve in particular! |
Yes, that should mostly work already now. The work needed is to integrate that setup nicely with sphinx-gallery and Jupyterlite, as that's not a typical workflow. |
* MAINT Clean deprecated losses in (hist) gradient boosting for 1.3 (scikit-learn#25834) * MAINT Clean deprecation of normalize in calibration_curve for 1.3 (scikit-learn#25833) * BLD Clean command removes generated from cython templates (scikit-learn#25839) * PERF Implement `PairwiseDistancesReduction` backend for `KNeighbors.predict_proba` (scikit-learn#24076) Signed-off-by: Julien Jerphanion <[email protected]> Co-authored-by: Julien Jerphanion <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> * MAINT Added Parameter Validation for datasets.make_circles (scikit-learn#25848) Co-authored-by: jeremiedbb <[email protected]> * MNT use a single job by default with sphinx build (scikit-learn#25836) * BLD Generate warning automatically for templated cython files (scikit-learn#25842) * MAINT parameter validation for sklearn.datasets.fetch_lfw_people (scikit-learn#25820) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameters validation for metrics.fbeta_score (scikit-learn#25841) * TST add global_random_seed fixture to sklearn/covariance/tests/test_robust_covariance.py (scikit-learn#25821) * MAINT Parameter validation for linear_model.orthogonal_mp (scikit-learn#25817) * TST activate common tests for TSNE (scikit-learn#25374) * CI Update lock files (scikit-learn#25849) * MAINT Added Parameter Validation for metrics.mean_gamma_deviance (scikit-learn#25853) * MAINT Parameters validation for feature_selection.mutual_info_regression (scikit-learn#25850) * MAINT parameter validation metrics.class_likelihood_ratios (scikit-learn#25863) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Ensure disjoint interval constraints (scikit-learn#25797) * MAINT Parameters validation for utils.gen_batches (scikit-learn#25864) * TST use global_random_seed in test_dict_vectorizer.py (scikit-learn#24533) * TST use global_random_seed in test_pls.py (scikit-learn#24526) Co-authored-by: jeremiedbb <[email protected]> * TST use global_random_seed in test_gpc.py (scikit-learn#24600) Co-authored-by: jeremiedbb <[email protected]> * DOC Fix overlapping plot axis in bench_sample_without_replacement.py (scikit-learn#25870) * MAINT Use contiguous memoryviews in _random.pyx (scikit-learn#25871) * MAINT parameter validation sklearn.datasets.fetch_lfw_pair (scikit-learn#25857) * MAINT Parameters validation for metrics.classification_report (scikit-learn#25868) * Empty commit * DOC fix docstring dtype parameter in OrdinalEncoder (scikit-learn#25877) * MAINT Clean up depreacted "log" loss of SGDClassifier for 1.3 (scikit-learn#25865) * ENH Adds TargetEncoder (scikit-learn#25334) Co-authored-by: Andreas Mueller <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Jovan Stojanovic <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> * CI make it possible to cancel running Azure jobs (scikit-learn#25876) * MAINT Clean-up deprecated if_delegate_has_method for 1.3 (scikit-learn#25879) * MAINT Parameter validation for tree.export_text (scikit-learn#25867) * DOC impact of `tol` for solvers in RidgeClassifier (scikit-learn#25530) * MAINT Parameters validation for metrics.hinge_loss (scikit-learn#25880) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for metrics.ndcg_score (scikit-learn#25885) * ENH KMeans initialization account for sample weights (scikit-learn#25752) Co-authored-by: jeremiedbb <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> Co-authored-by: Jérémie du Boisberranger <[email protected]> * TST use global_random_seed in sklearn/tests/test_dummy.py (scikit-learn#25884) * DOC improve calibration user guide (scikit-learn#25687) * ENH Support for sparse matrices added to `sklearn.metrics.silhouette_samples` (scikit-learn#24677) Co-authored-by: Sahil Gupta <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> * MAINT validate_params for plot_tree (scikit-learn#25882) Co-authored-by: Itay <[email protected]> * MAINT add missing space in error message in SVM (scikit-learn#25913) * FIX Adds requires_y tag to TargetEncoder (scikit-learn#25917) * MAINT Consistent cython types continued (scikit-learn#25810) * TST Speed-up common tests of DictionaryLearning (scikit-learn#25892) * TST Speed-up test_dbscan_optics_parity (scikit-learn#25893) * ENH add np.nan option for zero_division in precision/recall/f-score (scikit-learn#25531) Co-authored-by: Guillaume Lemaitre <[email protected]> * MAINT Parameters validation for datasets.make_low_rank_matrix (scikit-learn#25901) * MAINT Parameter validation for metrics.cluster.adjusted_mutual_info_score (scikit-learn#25898) Co-authored-by: Jérémie du Boisberranger <[email protected]> * TST Speed-up test_partial_dependence.test_output_shape (scikit-learn#25895) Co-authored-by: Thomas J. Fan <[email protected]> * MAINT Parameters validation for datasets.make_regression (scikit-learn#25899) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for metrics.mean_squared_log_error (scikit-learn#25924) * TST Use global_random_seed in tests/test_naive_bayes.py (scikit-learn#25890) * TST add global_random_seed fixture to sklearn/datasets/tests/test_covtype.py (scikit-learn#25904) Co-authored-by: Jérémie du Boisberranger <[email protected]> Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameters validation for datasets.make_multilabel_classification (scikit-learn#25920) * Fixed feature mapping typo (scikit-learn#25934) * MAINT switch to newer codecov uploader (scikit-learn#25919) Co-authored-by: Loïc Estève <[email protected]> * TST Speed-up test suite when using pytest-xdist (scikit-learn#25918) * DOC update license year to 2023 (scikit-learn#25936) * FIX Remove spurious feature names warning in IsolationForest (scikit-learn#25931) * TST fix unstable test_newrand_set_seed (scikit-learn#25940) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Clean-up deprecated max_features="auto" in trees/forests/gb (scikit-learn#25941) * MAINT LogisticRegression informative error msg when penaly=elasticnet and l1_ratio is None (scikit-learn#25925) Co-authored-by: jeremiedbb <[email protected]> * MAINT Clean-up remaining SGDClassifier(loss="log") (scikit-learn#25938) * FIX Fixes pandas extension arrays in check_array (scikit-learn#25813) * FIX Fixes pandas extension arrays with objects in check_array (scikit-learn#25814) * CI Disable pytest-xdist in pylatest_pip_openblas_pandas build (scikit-learn#25943) * MAINT remove deprecated call to resources.content (scikit-learn#25951) * DOC note on calibration impact on ranking (scikit-learn#25900) * Remove loguniform fix, use scipy.stats instead (scikit-learn#24665) Co-authored-by: Olivier Grisel <[email protected]> * MAINT Fix broken links in cluster.dbscan module (scikit-learn#25958) * DOC Fix lars Xy shape (scikit-learn#25952) * ENH Add drop_intermediate parameter to metrics.precision_recall_curve (scikit-learn#24668) Co-authored-by: Guillaume Lemaitre <[email protected]> * FIX improve error message when computing NDCG with a single document (scikit-learn#25672) Co-authored-by: Guillaume Lemaitre <[email protected]> * MAINT introduce _get_response_values and _check_response_methods (scikit-learn#23073) Co-authored-by: Thomas J. Fan <[email protected]> Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Extend message for large sparse matrices support (scikit-learn#25961) Co-authored-by: Meekail Zain <[email protected]> * MAINT Parameters validation for datasets.make_gaussian_quantiles (scikit-learn#25959) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.metrics.d2_tweedie_score (scikit-learn#25975) * MAINT Parameters validation for datasets.make_hastie_10_2 (scikit-learn#25967) * MAINT Parameters validation for preprocessing.minmax_scale (scikit-learn#25962) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for datasets.make_checkerboard (scikit-learn#25955) * MAINT Parameters validation for datasets.make_biclusters (scikit-learn#25945) * MAINT Parameters validation for datasets.make_moons (scikit-learn#25971) * DOC replace deviance by loss in docstring of GradientBoosting (scikit-learn#25968) * MAINT Fix broken link in feature_selection/_univariate_selection.py (scikit-learn#25984) * DOC Update model_persistence.rst to fix skops example (scikit-learn#25993) Co-authored-by: adrinjalali <[email protected]> * DOC Specified meaning for max_patches=None in extract_patches_2d (scikit-learn#25996) * DOC document that last step is never cached in pipeline (scikit-learn#25995) Co-authored-by: Guillaume Lemaitre <[email protected]> * FIX SequentialFeatureSelector throws IndexError when cv is a generator (scikit-learn#25973) * ENH Adds infrequent categories support to OrdinalEncoder (scikit-learn#25677) Co-authored-by: Tim Head <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Andreas Mueller <[email protected]> * MAINT make plot_digits_denoising deterministic by fixing random state (scikit-learn#26004) * DOC improve example of PatchExtractor (scikit-learn#26002) * MAINT Parameters validation for datasets.make_friedman2 (scikit-learn#25986) * MAINT Parameters validation for datasets.make_friedman3 (scikit-learn#25989) * MAINT Parameters validation for datasets.make_sparse_uncorrelated (scikit-learn#26001) * MAINT Parameters validation for datasets.make_spd_matrix (scikit-learn#26003) * MAINT Parameters validation for datasets.make_sparse_spd_matrix (scikit-learn#26009) * DOC Added the meanings of default=None for PatchExtractor parameters (scikit-learn#26005) * MAINT remove unecessary check covered by parameter validation framework (scikit-learn#26014) * MAINT Consistent cython types from _typedefs (scikit-learn#25942) Co-authored-by: Julien Jerphanion <[email protected]> * MAINT Parameters validation for datasets.make_swiss_roll (scikit-learn#26020) * MAINT Parameters validation for datasets.make_s_curve (scikit-learn#26022) * MAINT Parameters validation for datasets.make_blobs (scikit-learn#25983) Co-authored-by: Guillaume Lemaitre <[email protected]> * DOC fix SplineTransformer include_bias docstring (scikit-learn#26018) * ENH RocCurveDisplay add option to plot chance level (scikit-learn#25987) * DOC show from_estimator and from_predictions for Displays (scikit-learn#25994) * EXA Fix rst in plot_partial_dependence (scikit-learn#26028) * CI Adds coverage to docker jobs on Azure (scikit-learn#26027) Co-authored-by: Julien Jerphanion <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> * API Replace `n_iter` in `Bayesian Ridge` and `ARDRegression` (scikit-learn#25697) Co-authored-by: Guillaume Lemaitre <[email protected]> * CLN Make _NumPyAPIWrapper naming consistent to _ArrayAPIWrapper (scikit-learn#26039) * CI disable coverage on Windows to keep CI times reasonable (scikit-learn#26052) * DOC Use Scientific Python Plausible instance for analytics (scikit-learn#25547) * MAINT Parameters validation for sklearn.preprocessing.scale (scikit-learn#26036) * MAINT Parameters validation for sklearn.metrics.pairwise.haversine_distances (scikit-learn#26047) * MAINT Parameters validation for sklearn.metrics.pairwise.laplacian_kernel (scikit-learn#26048) * MAINT Parameters validation for sklearn.metrics.pairwise.linear_kernel (scikit-learn#26049) * MAINT Parameters validation for sklearn.metrics.silhouette_samples (scikit-learn#26053) * MAINT Parameters validation for sklearn.preprocessing.add_dummy_feature (scikit-learn#26058) * Added Parameter Validation for metrics.cluster.normalized_mutual_info_score() (scikit-learn#26060) * DOC Typos in HistGradientBoosting documentation (scikit-learn#26057) * TST add global_random_seed fixture to sklearn/datasets/tests/test_rcv1.py (scikit-learn#26043) * MAINT Parameters validation for sklearn.metrics.pairwise.cosine_similarity (scikit-learn#26006) Co-authored-by: Jérémie du Boisberranger <[email protected]> * ENH Adds isdtype to Array API wrapper (scikit-learn#26029) * MAINT Parameters validation for sklearn.metrics.silhouette_score (scikit-learn#26054) Co-authored-by: Jérémie du Boisberranger <[email protected]> * FIX fix spelling mistake in _NumPyAPIWrapper (scikit-learn#26064) * CI ignore more non-library Python files in codecov (scikit-learn#26059) * MAINT Parameters validation for sklearn.metrics.pairwise.cosine_distances (scikit-learn#26046) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Introduce BinaryClassifierCurveDisplayMixin (scikit-learn#25969) Co-authored-by: Jérémie du Boisberranger <[email protected]> * ENH Forces shape to be tuple when using Array API's reshape (scikit-learn#26030) Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Tim Head <[email protected]> * MAINT Parameters validation for sklearn.metrics.pairwise.paired_euclidean_distances (scikit-learn#26073) * MAINT Parameters validation for sklearn.metrics.pairwise.paired_manhattan_distances (scikit-learn#26074) * MAINT Parameters validation for sklearn.metrics.pairwise.paired_cosine_distances (scikit-learn#26075) * MAINT Parameters validation for sklearn.preprocessing.binarize (scikit-learn#26076) * MAINT Parameters validation for metrics.explained_variance_score (scikit-learn#26079) * DOC use correct template name for displays (scikit-learn#26081) * MAINT Parameters validation for sklearn.preprocessing.maxabs_scale (scikit-learn#26077) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.preprocessing.label_binarize (scikit-learn#26078) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT parameter validation for d2_absolute_error_score (scikit-learn#26066) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameter validation for roc_auc_score (scikit-learn#26007) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameters validation for sklearn.preprocessing.normalize (scikit-learn#26069) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameter validation for metrics.cluster.fowlkes_mallows_score (scikit-learn#26080) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameters validation for compose.make_column_transformer (scikit-learn#25897) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameters validation for sklearn.metrics.pairwise.polynomial_kernel (scikit-learn#26070) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.metrics.pairwise.rbf_kernel (scikit-learn#26071) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.metrics.pairwise.sigmoid_kernel (scikit-learn#26072) Co-authored-by: Jérémie du Boisberranger <[email protected]> * 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 <[email protected]> Co-authored-by: Tim Head <[email protected]> * 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 <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> * 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 <[email protected]> * 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 <[email protected]> * MAINT Parameters validation for sklearn.metrics.cluster.homogeneity_completeness_v_measure (scikit-learn#26137) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.metrics.rand_score (scikit-learn#26138) Co-authored-by: Jérémie du Boisberranger <[email protected]> * 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 <[email protected]> * 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 <[email protected]> * MAINT Parameters validation for sklearn.datasets.load_breast_cancer (scikit-learn#26165) Co-authored-by: Jérémie du Boisberranger <[email protected]> * 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 <[email protected]> * ASV Fix tol in SGDRegressorBenchmark (scikit-learn#26146) Co-authored-by: jeremie du boisberranger <[email protected]> * 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 <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]> * MAINT Parameters validation for sklearn.neighbors.sort_graph_by_row_values (scikit-learn#26173) Co-authored-by: Jérémie du Boisberranger <[email protected]> * FIX improve convergence criterion for LogisticRegression(penalty="l1", solver='liblinear') (scikit-learn#25214) Co-authored-by: Thomas J. Fan <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> * 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 <[email protected]> Co-authored-by: Jérémie du Boisberranger <[email protected]> * 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 <[email protected]> Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.datasets.load_digits (scikit-learn#26195) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.preprocessing.quantile_transform (scikit-learn#26144) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.model_selection.cross_validate (scikit-learn#26129) Co-authored-by: jeremiedbb <[email protected]> * DOC Adds TargetEncoder example explaining the internal CV (scikit-learn#26185) Co-authored-by: Tim Head <[email protected]> * spelling mistake corrected in documentation for script `plot_document_clustering.py` (scikit-learn#26228) Co-authored-by: Olivier Grisel <[email protected]> * FIX possible UnboundLocalError in fetch_openml (scikit-learn#26236) * ENH Adds PyTorch support to LinearDiscriminantAnalysis (scikit-learn#25956) Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Tim Head <[email protected]> * 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 <[email protected]> * 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 <[email protected]> * MAINT Parameters validation for sklearn.datasets.load_files (scikit-learn#26203) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.datasets.get_data_home (scikit-learn#26260) Co-authored-by: Jérémie du Boisberranger <[email protected]> * 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 <[email protected]> * 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 <[email protected]> * 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 <[email protected]> * MAINT Parameters validation for sklearn.inspection.partial_dependence (scikit-learn#26209) Co-authored-by: jeremie du boisberranger <[email protected]> * MAINT Parameters validation for sklearn.model_selection.validation_curve (scikit-learn#26229) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.model_selection.learning_curve (scikit-learn#26227) Co-authored-by: jeremie du boisberranger <[email protected]> * 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 <[email protected]> * MNT Improve error message when checking classification target is of a non-regression type (scikit-learn#26281) Co-authored-by: Adrin Jalali <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]> * 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 <[email protected]> Co-authored-by: Julien Jerphanion <[email protected]> * 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 <[email protected]> Co-authored-by: Adrin Jalali <[email protected]> * DOC Add HGBDT to "see also" section of random forests (scikit-learn#26319) Co-authored-by: ArturoAmorQ <[email protected]> Co-authored-by: Tim Head <[email protected]> * 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 <[email protected]> * ENH Allow for appropriate dtype us in `preprocessing.PolynomialFeatures` for sparse matrices (scikit-learn#23731) Co-authored-by: Aleksandr Kokhaniukov <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Julien Jerphanion <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]> * DOC Fix minor typo (scikit-learn#26327) * MAINT bump minimum version for pytest (scikit-learn#26184) Co-authored-by: Loïc Estève <[email protected]> Co-authored-by: Adrin Jalali <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> * 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 <[email protected]> * FIX make pipeline pass check_estimator (scikit-learn#26325) * FEA Add multiclass support to `average_precision_score` (scikit-learn#24769) Co-authored-by: Geoffrey <[email protected]> Co-authored-by: gbolmier <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]> --------- Signed-off-by: Julien Jerphanion <[email protected]> Co-authored-by: Jérémie du Boisberranger <[email protected]> Co-authored-by: Meekail Zain <[email protected]> Co-authored-by: Julien Jerphanion <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: zeeshan lone <[email protected]> Co-authored-by: jeremiedbb <[email protected]> Co-authored-by: Adrin Jalali <[email protected]> Co-authored-by: Shiva chauhan <[email protected]> Co-authored-by: AymericBasset <[email protected]> Co-authored-by: Maren Westermann <[email protected]> Co-authored-by: Nishu Choudhary <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> Co-authored-by: Loïc Estève <[email protected]> Co-authored-by: Benedek Harsanyi <[email protected]> Co-authored-by: Pooja Subramaniam <[email protected]> Co-authored-by: Rushil Desai <[email protected]> Co-authored-by: Xiao Yuan <[email protected]> Co-authored-by: Omar Salman <[email protected]> Co-authored-by: 2357juan <[email protected]> Co-authored-by: Théophile Baranger <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]> Co-authored-by: Andreas Mueller <[email protected]> Co-authored-by: Jovan Stojanovic <[email protected]> Co-authored-by: Rahil Parikh <[email protected]> Co-authored-by: Bharat Raghunathan <[email protected]> Co-authored-by: Sortofamudkip <[email protected]> Co-authored-by: Gleb Levitski <[email protected]> Co-authored-by: Christian Lorentzen <[email protected]> Co-authored-by: Ashwin Mathur <[email protected]> Co-authored-by: Sahil Gupta <[email protected]> Co-authored-by: Veghit <[email protected]> Co-authored-by: Itay <[email protected]> Co-authored-by: precondition <[email protected]> Co-authored-by: Marc Torrellas Socastro <[email protected]> Co-authored-by: Dominic Fox <[email protected]> Co-authored-by: futurewarning <[email protected]> Co-authored-by: Yao Xiao <[email protected]> Co-authored-by: Joey Ortiz <[email protected]> Co-authored-by: Tim Head <[email protected]> Co-authored-by: Christian Veenhuis <[email protected]> Co-authored-by: adienes <[email protected]> Co-authored-by: Dave Berenbaum <[email protected]> Co-authored-by: Lene Preuss <[email protected]> Co-authored-by: A.H.Mansouri <[email protected]> Co-authored-by: Boris Feld <[email protected]> Co-authored-by: Carla J <[email protected]> Co-authored-by: windiana42 <[email protected]> Co-authored-by: mdarii <[email protected]> Co-authored-by: murezzda <[email protected]> Co-authored-by: Peter Piontek <[email protected]> Co-authored-by: John Pangas <[email protected]> Co-authored-by: Dmitry Nesterov <[email protected]> Co-authored-by: Yuchen Zhou <[email protected]> Co-authored-by: Ekaterina Butyugina <[email protected]> Co-authored-by: Jiawei Zhang <[email protected]> Co-authored-by: Ansam Zedan <[email protected]> Co-authored-by: genvalen <[email protected]> Co-authored-by: farhan khan <[email protected]> Co-authored-by: Arturo Amor <[email protected]> Co-authored-by: Jiawei Zhang <[email protected]> Co-authored-by: Ralf Gommers <[email protected]> Co-authored-by: Jessicakk0711 <[email protected]> Co-authored-by: Ankur Singh <[email protected]> Co-authored-by: Seoeun(Sun☀️) Hong <[email protected]> Co-authored-by: Nightwalkx <[email protected]> Co-authored-by: VIGNESH D <[email protected]> Co-authored-by: Vincent-violet <[email protected]> Co-authored-by: Elabonga Atuo <[email protected]> Co-authored-by: Tom Dupré la Tour <[email protected]> Co-authored-by: André Pedersen <[email protected]> Co-authored-by: Ashish Dutt <[email protected]> Co-authored-by: Phil <[email protected]> Co-authored-by: Stanislav (Stanley) Modrak <[email protected]> Co-authored-by: hujiahong726 <[email protected]> Co-authored-by: James Dean <[email protected]> Co-authored-by: ArturoAmorQ <[email protected]> Co-authored-by: Aleksandr Kokhaniukov <[email protected]> Co-authored-by: c-git <[email protected]> Co-authored-by: annegnx <[email protected]> Co-authored-by: Geoffrey <[email protected]> Co-authored-by: gbolmier <[email protected]>
This is a draft PR to use sphinx-gallery Jupyterlite integration sphinx-gallery/sphinx-gallery#977 and detect possible issues.
Unfortunately CircleCI does not allow JupyterLite to work in artifacts, so for now I will manually run
make html-noplotlocally and make the output accessible at http://lesteve.github.io/scikit-learn.See sphinx-gallery/sphinx-gallery#977 (comment) for more details about the CircleCI limitation.