Drop machine_learning/xgboost_classifier.py and
machine_learning/xgboost_regressor.py. Both were thin "how-to-use"
wrappers around sklearn's XGBClassifier/XGBRegressor rather than
from-scratch implementations, and the classifier's only doctest was
already disabled (# THIS TEST IS BROKEN!!), so it was never exercised
in CI.
xgboost is one of the heaviest compiled dependencies in the tree (large
wheel, needs OpenMP/libgomp at runtime, no free-threaded wheel yet), and
gradient boosting is already implemented from scratch in
machine_learning/gradient_boosting_classifier.py and
gradient_boosting_regressor.py, so no algorithm coverage is lost.
Removes the xgboost dependency from pyproject.toml, its (and its
xgboost-only transitive dep nvidia-nccl-cu13) entries from uv.lock, and
the two DIRECTORY.md links.
Refs #15081
Re-enable the four scikit-learn machine-learning examples and the
neural-network perceptron that had been disabled (renamed to
.broken.txt / .DISABLED), and modernize them so they import and run
cleanly on current scikit-learn and pass the doctest CI:
machine_learning/gaussian_naive_bayes.py
machine_learning/random_forest_classifier.py
- Replace the removed sklearn.metrics.plot_confusion_matrix with
ConfusionMatrixDisplay.from_estimator (removed in scikit-learn 1.2).
- Drop the artificial time.sleep() calls.
machine_learning/gradient_boosting_regressor.py
machine_learning/random_forest_regressor.py
- Replace the removed load_boston dataset (removed in scikit-learn
1.2 for ethical reasons) with the bundled load_diabetes dataset so
the examples run offline.
- Avoid an unused-variable lint (RUF059).
neural_network/perceptron.py
- Use a dedicated seeded random.Random instance instead of the global
random state, so training is reproducible and thread-safe under the
parallel test runner.
- Cap training at epoch_number epochs so it always terminates even on
non-linearly-separable data (previously an unbounded while True).
- Have training() and sort() return their results instead of printing,
per the contribution guidelines, and update the doctests accordingly.
Requested by @cclauss in #8029; perceptron follow-up to #15206.
* add a code file of the multi-layer perceptron classifier from scrach
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Delete tqdm in multilayer_perceptron_classifier_from_scratch.py
* Update multilayer_perceptron_classifier_from_scratch.py
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* Update multilayer_perceptron_classifier_from_scratch.py
add a code file of the multi-layer perceptron classifier from scrach
* Correct errors in multilayer_perceptron_classifier_from_scratch.py
* Delete machine_learning/multilayer_perceptron_classifier.py
* Rename multilayer_perceptron_classifier_from_scratch.py
* Apply suggestion from @cclauss
---------
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* deps: migrate from httpx to httpx2 (pydantic's maintained fork)
Mechanical rename of httpx -> httpx2 (API-compatible fork of httpx 0.28.1):
pyproject.toml deps, PEP 723 inline-script headers, and all import/call sites.
Excludes uv.lock (the keeper's allow-list rejects .lock files); the lock
refresh needs a separate maintainer-merged PR.
Refs #15081
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* deps: drop tweepy + migrate remaining requests refs to httpx2
- maths/allocation_number.py: docstring example uses httpx2, not requests
- web_programming/get_imdbtop.py.DISABLED: import httpx2 instead of requests
- remove web_programming/get_user_tweets.py.DISABLED (a Twitter API how-to,
not an algorithm) and drop the tweepy dependency that was its only user and
the last high-level dep pulling in requests
- uv.lock intentionally untouched (keeper allow-list)
Refs #15081
---------
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* ci: un-ignore local_weighted_learning doctests in build.yml
machine_learning/local_weighted_learning/local_weighted_learning.py only
imports numpy and matplotlib (both already project dependencies) and its
5 doctests pass headlessly. Removing it from the pytest --ignore list so
the module is covered by CI again.
* fix(local_weighted_learning): use a well-conditioned bandwidth in doctests
The doctests used tau=0.6 on data with feature values ~17-25, so the
Gaussian weights underflowed to ~0 (e.g. 8e-118, 1e-177). That made
X\u1d40WX numerically singular (cond ~5.6e18), so its inverse - and the
resulting predictions - were nondeterministic across numpy/BLAS builds.
That is why the module was on the pytest --ignore list; on the CI numpy
the first prediction came out 0.0 instead of the documented 1.07.
Switch the doctests to tau=5 (cond ~2e2), matching the bandwidth the
module's own main() already uses, and round the outputs so they are
stable across platforms. Deterministic now; removed from --ignore.
`machine_learning/automatic_differentiation.py` was the only module
importing `Self` from `typing_extensions`, behind a `# noqa: UP035`.
Since the repo requires Python >=3.14, `typing.Self` (added in 3.11)
is always available, so import it from the stdlib and drop the noqa.
### Describe your change:
* [x] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [ ] Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. If not, please split into separate PRs.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python type hints.
* [x] All functions have doctests that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* fix: raise ValueError in encode() for non-lowercase input
encode() previously accepted uppercase letters, digits, and other
non-lowercase characters silently, producing incorrect/out-of-range
values (e.g. negative numbers for uppercase letters) instead of
failing. Add input validation using str.islower() and str.isalpha()
to raise a ValueError when the input isn't purely lowercase a-z.
Added a doctest covering the new error case.
* resolved doctest
* Fix Ruff 0.16 lint failures
* Enhance encode function error handling examples
Update error handling in encode function to include examples for mixed case and invalid characters.
* Fix indentation in test_cancer_data function
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* feat: optimizing the prune function at the apriori_algorithm.py archive
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* fix: fixing the unsorted importing statment
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: fixing the key structure to a tuple that can be an hashable structure
* Update apriori_algorithm.py
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* Update apriori_algorithm.py
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* Added t-SNE with Iris dataset example
* Added t-SNE with Iris dataset example
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* Updated with descriptive variables
* Add descriptive variable names
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Add Descriptive Variable names
* Adding Descriptive variable names
* Update machine_learning/t_stochastic_neighbour_embedding.py
Co-authored-by: Christian Clauss <cclauss@me.com>
* Update machine_learning/t_stochastic_neighbour_embedding.py
Co-authored-by: Christian Clauss <cclauss@me.com>
* Improved line formatting
* Adding URL for t-SNE Wikipedia
* Apply suggestion from @cclauss
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* pre-commit autoupdate 2025-09-11
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- Added PCA implementation with dataset standardization.
- Used Singular Value Decomposition (SVD) for computing principal components.
- Fixed import sorting to comply with PEP 8 (Ruff I001).
- Ensured type hints and docstrings for better readability.
- Added doctests to validate correctness.
- Passed all Ruff checks and automated tests.
* [pre-commit.ci] pre-commit autoupdate
updates:
- [github.com/astral-sh/ruff-pre-commit: v0.8.6 → v0.9.1](https://github.com/astral-sh/ruff-pre-commit/compare/v0.8.6...v0.9.1)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Update maths/dual_number_automatic_differentiation.py
* Update maths/dual_number_automatic_differentiation.py
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Update dual_number_automatic_differentiation.py
* Update dual_number_automatic_differentiation.py
* No <fin-streamer> tag with the specified data-test attribute found.
---------
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* issue #11150 Ensure explicit column selection and data type setting in data reading process.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* updating DIRECTORY.md
* Fix some SIM114 per file ignores
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix review issue
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* Enable ruff S113 rule
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* Enable ruff E741 rule
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* Enable ruff ICN001 rule
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* Enable ruff PLR5501 rule
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* fix: function name typo
Signed-off-by: guoguangwu <guoguangwug@gmail.com>
* lfu_cache.py: Use f-strings
* rsa_cipher.py: Use f-strings
---------
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* Added Gradient Boosting Classifier
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Update gradient_boosting_classifier.py
* Update gradient_boosting_classifier.py
* Update gradient_boosting_classifier.py
* Update gradient_boosting_classifier.py
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* Added automatic differentiation algorithm
* file name changed
* Resolved pre commit errors
* updated dependency
* added noqa for ignoring check
* adding typing_extension for adding Self type in __new__
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* sorted requirement.text dependency
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* resolved ruff
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* added mean absolute error to loss_functions.py
* added doctest to mean absolute error to loss_functions.py
* fixed long line in loss_functions.py
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fixed error in MAE
* Update machine_learning/loss_functions.py
Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com>
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* added mean absolute percentage error
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* added mean_absolute_percentage_error
* added mean_absolute_percentage_error
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* added mean_absolute_percentage_error
* added mean_absolute_percentage_error
* added mean absolute percentage error
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* added mean absolute percentage error
* added mean absolute percentage error
* added mean absolute percentage error
* added mean absolute percentage error
* added mean absolute percentage error
* Update machine_learning/loss_functions.py
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* Added Binary Focal Cross Entropy
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* Fixed Issue
* Fixed Issue
* Added BFCE loss to loss_functions.py
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* Update machine_learning/loss_functions.py
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