From 96ab82459250db9eddf0f099f557387d2bfaabda Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Krsto=20Prorokovi=C4=87?= Date: Fri, 16 May 2025 18:26:56 +0200 Subject: [PATCH 1/5] Remove __lt__ from ClusterNode --- unsupervised_bias_detection/cluster/_bahc.py | 24 +++++++++---------- .../cluster/_cluster_node.py | 7 +----- 2 files changed, 12 insertions(+), 19 deletions(-) diff --git a/unsupervised_bias_detection/cluster/_bahc.py b/unsupervised_bias_detection/cluster/_bahc.py index 45f2059..bf859b2 100644 --- a/unsupervised_bias_detection/cluster/_bahc.py +++ b/unsupervised_bias_detection/cluster/_bahc.py @@ -71,16 +71,15 @@ def fit(self, X, y): std = np.std(y) # The entire dataset has a discrimination score of zero score = 0 - root = ClusterNode(label, -std, score) + root = ClusterNode(label, score) self.cluster_tree_ = root - heap = [root] + heap = [(-std, label, root)] for _ in range(self.bahc_max_iter): if not heap: # If the heap is empty we stop iterating break # Take the cluster with the highest standard deviation of metric y - node = heapq.heappop(heap) - label = node.label + _, label, node = heapq.heappop(heap) score = node.score cluster_indices = np.nonzero(labels == label)[0] X_cluster = X[cluster_indices] @@ -121,21 +120,20 @@ def fit(self, X, y): # If the split is valid, we create the children nodes and split the current node # Otherwise, we add the current node to the leaves if valid_split: - # TODO: Make this nicer! - # TODO: Maybe explain why we negate std before pushing to heap first_child_indices = children_indices[0] - first_child_std = np.std(y[first_child_indices]) first_child_score = child_scores[0] - first_child = ClusterNode(label, -first_child_std, first_child_score) - heapq.heappush(heap, first_child) + first_child = ClusterNode(label, first_child_score) + first_child_std = np.std(y[first_child_indices]) + # heapq implements min-heap, so we negate std before pushing + heapq.heappush(heap, (-first_child_std, label, first_child)) labels[first_child_indices] = label children = [first_child] for i in range(1, n_children): child_indices = children_indices[i] - child_std = np.std(y[child_indices]) child_score = child_scores[i] - child_node = ClusterNode(self.n_clusters_, -child_std, child_score) - heapq.heappush(heap, child_node) + child_node = ClusterNode(self.n_clusters_, child_score) + child_std = np.std(y[child_indices]) + heapq.heappush(heap, (-child_std, self.n_clusters_, child_node)) labels[child_indices] = self.n_clusters_ children.append(child_node) self.n_clusters_ += 1 @@ -143,7 +141,7 @@ def fit(self, X, y): else: leaves.append(node) - leaves.extend(heap) + leaves.extend([leaf for _, _, leaf in heap]) leaf_scores = np.array([leaf.score for leaf in leaves]) # We sort clusters by decreasing scores sorted_indices = np.argsort(-leaf_scores) diff --git a/unsupervised_bias_detection/cluster/_cluster_node.py b/unsupervised_bias_detection/cluster/_cluster_node.py index d4d0398..de8dc0b 100644 --- a/unsupervised_bias_detection/cluster/_cluster_node.py +++ b/unsupervised_bias_detection/cluster/_cluster_node.py @@ -3,7 +3,7 @@ from typing import Self class ClusterNode: - def __init__(self, label: int, neg_std: float, score: float): + def __init__(self, label: int, score: float): """ Initialize a node in the cluster tree. @@ -13,7 +13,6 @@ def __init__(self, label: int, neg_std: float, score: float): The cluster label for this node (required as all nodes start as leaves) """ self.label = label - self.neg_std = neg_std self.score = score self.clustering_model = None self.children = [] @@ -22,10 +21,6 @@ def __init__(self, label: int, neg_std: float, score: float): def is_leaf(self): return len(self.children) == 0 - def __lt__(self, other: Self): - # TODO: Use score before label - return self.neg_std < other.neg_std or (self.neg_std == other.neg_std and self.label < other.label) - def split(self, clustering_model: ClusterMixin, children: list[Self]): """ Split this node by setting its clustering model and adding children. From a7a096fefd214d90280b1415d21379376c501a48 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Krsto=20Prorokovi=C4=87?= Date: Sat, 14 Mar 2026 17:57:14 +0100 Subject: [PATCH 2/5] Migrate from poetry to uv in Release Workflow --- .github/workflows/ci.yml | 34 +- .github/workflows/release.yml | 19 +- poetry.lock | 453 ----------------- pyproject.toml | 143 +++++- tests/__init__.py | 1 + tests/test_bahc.py | 79 --- tests/test_categorical.py | 113 +++++ tests/test_dataset.py | 2 +- tests/test_numerical.py | 113 +++++ tests/test_validation.py | 6 +- unsupervised_bias_detection/__init__.py | 2 +- .../cluster/__init__.py | 2 +- unsupervised_bias_detection/cluster/_bahc.py | 142 ++++-- .../cluster/_cluster_node.py | 23 +- .../cluster/_kmeans.py | 29 +- .../cluster/_kmodes.py | 39 +- unsupervised_bias_detection/utils/__init__.py | 4 +- .../utils/_get_column_dtypes.py | 20 +- unsupervised_bias_detection/utils/dataset.py | 29 +- .../utils/validation.py | 10 +- uv.lock | 454 ++++++++++++++++++ 21 files changed, 1027 insertions(+), 690 deletions(-) delete mode 100644 poetry.lock delete mode 100644 tests/test_bahc.py create mode 100644 tests/test_categorical.py create mode 100644 tests/test_numerical.py create mode 100644 uv.lock diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 8feb925..1ffb50e 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -10,35 +10,27 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ["3.11"] + python-version: + - 3.11 + - 3.12 + - 3.13 + - 3.14 steps: - name: Checkout repository - uses: actions/checkout@v4 + uses: actions/checkout@v6 - - name: Install poetry - run: | - pipx install poetry - poetry config virtualenvs.path .virtualenvs - - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 + - name: Install uv + uses: astral-sh/setup-uv@v7 with: python-version: ${{ matrix.python-version }} - cache: poetry - cache-dependency-path: poetry.lock - - - name: Set poetry environment - run: poetry env use ${{ matrix.python-version }} + enable-cache: true - name: Install dependencies - run: poetry install --no-root --no-interaction + run: uv sync --locked - name: Lint - run: poetry run ruff check unsupervised_bias_detection + if: matrix.python-version == 3.11 + run: uv run ruff check unsupervised_bias_detection/cluster - name: Test - run: poetry run pytest - --color=yes - --full-trace - --showlocals - --verbose + run: uv run pytest \ No newline at end of file diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index df4e55f..c9115b0 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -9,19 +9,12 @@ jobs: runs-on: ubuntu-latest steps: - name: Checkout repository - uses: actions/checkout@v4 + uses: actions/checkout@v6 - - name: Install poetry - run: | - pipx install poetry - # poetry config virtualenvs.path .virtualenvs - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: "3.11" + - name: Install uv + uses: astral-sh/setup-uv@v7 - - name: Publish package to PyPI + - name: Build and publish + run: uv build && uv publish env: - PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }} - run: poetry publish --build --username "__token__" --password $PYPI_TOKEN \ No newline at end of file + UV_PUBLISH_TOKEN: ${{ secrets.PYPI_TOKEN }} \ No newline at end of file diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 41f7534..0000000 --- a/poetry.lock +++ /dev/null @@ -1,453 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. - -[[package]] -name = "colorama" -version = "0.4.6" -description = "Cross-platform colored terminal text." -optional = false -python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" -files = [ - {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"}, - {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, -] - -[[package]] -name = "fairlearn" -version = "0.10.0" -description = "A Python package to assess and improve fairness of machine learning models." -optional = false -python-versions = ">=3.8" -files = [ - {file = "fairlearn-0.10.0-py3-none-any.whl", hash = "sha256:772224097f8c073168bde44e659d7a2107f96d608063a738df9c985e17dab30f"}, - {file = "fairlearn-0.10.0.tar.gz", hash = "sha256:70e7aefaf9cb16e00462624d58b0517397970dc40d4cbc71e8d40f7c69800f9d"}, -] - -[package.dependencies] -numpy = ">=1.24.4" -pandas = ">=2.0.3" -scikit-learn = ">=1.2.1" -scipy = ">=1.9.3" - -[[package]] -name = "iniconfig" -version = "2.0.0" -description = "brain-dead simple config-ini parsing" -optional = false -python-versions = ">=3.7" -files = [ - {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"}, - {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"}, -] - -[[package]] -name = "joblib" -version = "1.4.2" -description = "Lightweight pipelining with Python functions" -optional = false -python-versions = ">=3.8" -files = [ - {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"}, - {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"}, -] - -[[package]] -name = "kmodes" -version = "0.12.2" -description = "Python implementations of the k-modes and k-prototypes clustering algorithms for clustering categorical data." -optional = false -python-versions = "*" -files = [ - {file = "kmodes-0.12.2-py2.py3-none-any.whl", hash = "sha256:b764f7166dd5fe63826135ed74df796693dc7c25fc2cb8a106e14f3bfb371004"}, - {file = "kmodes-0.12.2.tar.gz", hash = "sha256:d840ac9f4616a668ebacba24a12ec1def87da24a9fd0a0dc2f7499a9b9a6f45b"}, -] - -[package.dependencies] -joblib = ">=0.11" -numpy = ">=1.10.4" -scikit-learn = ">=0.22.0" -scipy = ">=0.13.3" - -[package.extras] -dev = ["pandas", "pytest", "pytest-cov"] - -[[package]] -name = "numpy" -version = "1.26.4" -description = "Fundamental package for array computing in Python" -optional = false -python-versions = ">=3.9" -files = [ - {file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"}, - {file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"}, - {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d209d8969599b27ad20994c8e41936ee0964e6da07478d6c35016bc386b66ad4"}, - {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f"}, - {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:62b8e4b1e28009ef2846b4c7852046736bab361f7aeadeb6a5b89ebec3c7055a"}, - {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a4abb4f9001ad2858e7ac189089c42178fcce737e4169dc61321660f1a96c7d2"}, - {file = "numpy-1.26.4-cp310-cp310-win32.whl", hash = "sha256:bfe25acf8b437eb2a8b2d49d443800a5f18508cd811fea3181723922a8a82b07"}, - {file = "numpy-1.26.4-cp310-cp310-win_amd64.whl", hash = "sha256:b97fe8060236edf3662adfc2c633f56a08ae30560c56310562cb4f95500022d5"}, - {file = "numpy-1.26.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4c66707fabe114439db9068ee468c26bbdf909cac0fb58686a42a24de1760c71"}, - {file = "numpy-1.26.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef"}, - {file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7ab55401287bfec946ced39700c053796e7cc0e3acbef09993a9ad2adba6ca6e"}, - {file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:666dbfb6ec68962c033a450943ded891bed2d54e6755e35e5835d63f4f6931d5"}, - {file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:96ff0b2ad353d8f990b63294c8986f1ec3cb19d749234014f4e7eb0112ceba5a"}, - {file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:60dedbb91afcbfdc9bc0b1f3f402804070deed7392c23eb7a7f07fa857868e8a"}, - {file = "numpy-1.26.4-cp311-cp311-win32.whl", hash = "sha256:1af303d6b2210eb850fcf03064d364652b7120803a0b872f5211f5234b399f20"}, - {file = "numpy-1.26.4-cp311-cp311-win_amd64.whl", hash = "sha256:cd25bcecc4974d09257ffcd1f098ee778f7834c3ad767fe5db785be9a4aa9cb2"}, - {file = "numpy-1.26.4-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b3ce300f3644fb06443ee2222c2201dd3a89ea6040541412b8fa189341847218"}, - {file = "numpy-1.26.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b"}, - {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9fad7dcb1aac3c7f0584a5a8133e3a43eeb2fe127f47e3632d43d677c66c102b"}, - {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:675d61ffbfa78604709862923189bad94014bef562cc35cf61d3a07bba02a7ed"}, - {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:ab47dbe5cc8210f55aa58e4805fe224dac469cde56b9f731a4c098b91917159a"}, - {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:1dda2e7b4ec9dd512f84935c5f126c8bd8b9f2fc001e9f54af255e8c5f16b0e0"}, - {file = "numpy-1.26.4-cp312-cp312-win32.whl", hash = "sha256:50193e430acfc1346175fcbdaa28ffec49947a06918b7b92130744e81e640110"}, - {file = "numpy-1.26.4-cp312-cp312-win_amd64.whl", hash = "sha256:08beddf13648eb95f8d867350f6a018a4be2e5ad54c8d8caed89ebca558b2818"}, - {file = "numpy-1.26.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7349ab0fa0c429c82442a27a9673fc802ffdb7c7775fad780226cb234965e53c"}, - {file = "numpy-1.26.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:52b8b60467cd7dd1e9ed082188b4e6bb35aa5cdd01777621a1658910745b90be"}, - {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d5241e0a80d808d70546c697135da2c613f30e28251ff8307eb72ba696945764"}, - {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3"}, - {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:679b0076f67ecc0138fd2ede3a8fd196dddc2ad3254069bcb9faf9a79b1cebcd"}, - {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:47711010ad8555514b434df65f7d7b076bb8261df1ca9bb78f53d3b2db02e95c"}, - {file = "numpy-1.26.4-cp39-cp39-win32.whl", hash = "sha256:a354325ee03388678242a4d7ebcd08b5c727033fcff3b2f536aea978e15ee9e6"}, - {file = "numpy-1.26.4-cp39-cp39-win_amd64.whl", hash = "sha256:3373d5d70a5fe74a2c1bb6d2cfd9609ecf686d47a2d7b1d37a8f3b6bf6003aea"}, - {file = "numpy-1.26.4-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:afedb719a9dcfc7eaf2287b839d8198e06dcd4cb5d276a3df279231138e83d30"}, - {file = "numpy-1.26.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95a7476c59002f2f6c590b9b7b998306fba6a5aa646b1e22ddfeaf8f78c3a29c"}, - {file = "numpy-1.26.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:7e50d0a0cc3189f9cb0aeb3a6a6af18c16f59f004b866cd2be1c14b36134a4a0"}, - {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"}, -] - -[[package]] -name = "packaging" -version = "24.2" -description = "Core utilities for Python packages" -optional = false -python-versions = ">=3.8" -files = [ - {file = "packaging-24.2-py3-none-any.whl", hash = "sha256:09abb1bccd265c01f4a3aa3f7a7db064b36514d2cba19a2f694fe6150451a759"}, - {file = "packaging-24.2.tar.gz", hash = "sha256:c228a6dc5e932d346bc5739379109d49e8853dd8223571c7c5b55260edc0b97f"}, -] - -[[package]] -name = "pandas" -version = "2.2.3" -description = "Powerful data structures for data analysis, time series, and statistics" -optional = false -python-versions = ">=3.9" -files = [ - {file = "pandas-2.2.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1948ddde24197a0f7add2bdc4ca83bf2b1ef84a1bc8ccffd95eda17fd836ecb5"}, - {file = "pandas-2.2.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:381175499d3802cde0eabbaf6324cce0c4f5d52ca6f8c377c29ad442f50f6348"}, - {file = "pandas-2.2.3-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d9c45366def9a3dd85a6454c0e7908f2b3b8e9c138f5dc38fed7ce720d8453ed"}, - {file = "pandas-2.2.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:86976a1c5b25ae3f8ccae3a5306e443569ee3c3faf444dfd0f41cda24667ad57"}, - {file = "pandas-2.2.3-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:b8661b0238a69d7aafe156b7fa86c44b881387509653fdf857bebc5e4008ad42"}, - {file = "pandas-2.2.3-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:37e0aced3e8f539eccf2e099f65cdb9c8aa85109b0be6e93e2baff94264bdc6f"}, - {file = "pandas-2.2.3-cp310-cp310-win_amd64.whl", hash = "sha256:56534ce0746a58afaf7942ba4863e0ef81c9c50d3f0ae93e9497d6a41a057645"}, - {file = "pandas-2.2.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:66108071e1b935240e74525006034333f98bcdb87ea116de573a6a0dccb6c039"}, - {file = "pandas-2.2.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7c2875855b0ff77b2a64a0365e24455d9990730d6431b9e0ee18ad8acee13dbd"}, - {file = "pandas-2.2.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cd8d0c3be0515c12fed0bdbae072551c8b54b7192c7b1fda0ba56059a0179698"}, - {file = "pandas-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c124333816c3a9b03fbeef3a9f230ba9a737e9e5bb4060aa2107a86cc0a497fc"}, - {file = "pandas-2.2.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:63cc132e40a2e084cf01adf0775b15ac515ba905d7dcca47e9a251819c575ef3"}, - {file = "pandas-2.2.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:29401dbfa9ad77319367d36940cd8a0b3a11aba16063e39632d98b0e931ddf32"}, - {file = "pandas-2.2.3-cp311-cp311-win_amd64.whl", hash = "sha256:3fc6873a41186404dad67245896a6e440baacc92f5b716ccd1bc9ed2995ab2c5"}, - {file = "pandas-2.2.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b1d432e8d08679a40e2a6d8b2f9770a5c21793a6f9f47fdd52c5ce1948a5a8a9"}, - {file = "pandas-2.2.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:a5a1595fe639f5988ba6a8e5bc9649af3baf26df3998a0abe56c02609392e0a4"}, - {file = "pandas-2.2.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:5de54125a92bb4d1c051c0659e6fcb75256bf799a732a87184e5ea503965bce3"}, - {file = "pandas-2.2.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fffb8ae78d8af97f849404f21411c95062db1496aeb3e56f146f0355c9989319"}, - {file = "pandas-2.2.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:6dfcb5ee8d4d50c06a51c2fffa6cff6272098ad6540aed1a76d15fb9318194d8"}, - {file = "pandas-2.2.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:062309c1b9ea12a50e8ce661145c6aab431b1e99530d3cd60640e255778bd43a"}, - {file = "pandas-2.2.3-cp312-cp312-win_amd64.whl", hash = "sha256:59ef3764d0fe818125a5097d2ae867ca3fa64df032331b7e0917cf5d7bf66b13"}, - {file = "pandas-2.2.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f00d1345d84d8c86a63e476bb4955e46458b304b9575dcf71102b5c705320015"}, - {file = "pandas-2.2.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:3508d914817e153ad359d7e069d752cdd736a247c322d932eb89e6bc84217f28"}, - {file = "pandas-2.2.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:22a9d949bfc9a502d320aa04e5d02feab689d61da4e7764b62c30b991c42c5f0"}, - {file = "pandas-2.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f3a255b2c19987fbbe62a9dfd6cff7ff2aa9ccab3fc75218fd4b7530f01efa24"}, - {file = "pandas-2.2.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:800250ecdadb6d9c78eae4990da62743b857b470883fa27f652db8bdde7f6659"}, - {file = "pandas-2.2.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6374c452ff3ec675a8f46fd9ab25c4ad0ba590b71cf0656f8b6daa5202bca3fb"}, - {file = "pandas-2.2.3-cp313-cp313-win_amd64.whl", hash = "sha256:61c5ad4043f791b61dd4752191d9f07f0ae412515d59ba8f005832a532f8736d"}, - {file = "pandas-2.2.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:3b71f27954685ee685317063bf13c7709a7ba74fc996b84fc6821c59b0f06468"}, - {file = "pandas-2.2.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:38cf8125c40dae9d5acc10fa66af8ea6fdf760b2714ee482ca691fc66e6fcb18"}, - {file = "pandas-2.2.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ba96630bc17c875161df3818780af30e43be9b166ce51c9a18c1feae342906c2"}, - {file = "pandas-2.2.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1db71525a1538b30142094edb9adc10be3f3e176748cd7acc2240c2f2e5aa3a4"}, - {file = "pandas-2.2.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:15c0e1e02e93116177d29ff83e8b1619c93ddc9c49083f237d4312337a61165d"}, - {file = "pandas-2.2.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:ad5b65698ab28ed8d7f18790a0dc58005c7629f227be9ecc1072aa74c0c1d43a"}, - {file = "pandas-2.2.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:bc6b93f9b966093cb0fd62ff1a7e4c09e6d546ad7c1de191767baffc57628f39"}, - {file = "pandas-2.2.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:5dbca4c1acd72e8eeef4753eeca07de9b1db4f398669d5994086f788a5d7cc30"}, - {file = "pandas-2.2.3-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:8cd6d7cc958a3910f934ea8dbdf17b2364827bb4dafc38ce6eef6bb3d65ff09c"}, - {file = "pandas-2.2.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:99df71520d25fade9db7c1076ac94eb994f4d2673ef2aa2e86ee039b6746d20c"}, - {file = "pandas-2.2.3-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:31d0ced62d4ea3e231a9f228366919a5ea0b07440d9d4dac345376fd8e1477ea"}, - {file = "pandas-2.2.3-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:7eee9e7cea6adf3e3d24e304ac6b8300646e2a5d1cd3a3c2abed9101b0846761"}, - {file = "pandas-2.2.3-cp39-cp39-win_amd64.whl", hash = "sha256:4850ba03528b6dd51d6c5d273c46f183f39a9baf3f0143e566b89450965b105e"}, - {file = "pandas-2.2.3.tar.gz", hash = "sha256:4f18ba62b61d7e192368b84517265a99b4d7ee8912f8708660fb4a366cc82667"}, -] - -[package.dependencies] -numpy = [ - {version = ">=1.23.2", markers = "python_version == \"3.11\""}, - {version = ">=1.26.0", markers = "python_version >= \"3.12\""}, -] -python-dateutil = ">=2.8.2" -pytz = ">=2020.1" -tzdata = ">=2022.7" - -[package.extras] -all = ["PyQt5 (>=5.15.9)", "SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)", "beautifulsoup4 (>=4.11.2)", "bottleneck (>=1.3.6)", "dataframe-api-compat (>=0.1.7)", "fastparquet (>=2022.12.0)", "fsspec (>=2022.11.0)", "gcsfs (>=2022.11.0)", "html5lib (>=1.1)", "hypothesis (>=6.46.1)", "jinja2 (>=3.1.2)", "lxml (>=4.9.2)", "matplotlib (>=3.6.3)", "numba (>=0.56.4)", "numexpr (>=2.8.4)", "odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "pandas-gbq (>=0.19.0)", "psycopg2 (>=2.9.6)", "pyarrow (>=10.0.1)", "pymysql (>=1.0.2)", "pyreadstat (>=1.2.0)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "qtpy (>=2.3.0)", "s3fs (>=2022.11.0)", "scipy (>=1.10.0)", "tables (>=3.8.0)", "tabulate (>=0.9.0)", "xarray (>=2022.12.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)", "zstandard (>=0.19.0)"] -aws = ["s3fs (>=2022.11.0)"] -clipboard = ["PyQt5 (>=5.15.9)", "qtpy (>=2.3.0)"] -compression = ["zstandard (>=0.19.0)"] -computation = ["scipy (>=1.10.0)", "xarray (>=2022.12.0)"] -consortium-standard = ["dataframe-api-compat (>=0.1.7)"] -excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)"] -feather = ["pyarrow (>=10.0.1)"] -fss = ["fsspec (>=2022.11.0)"] -gcp = ["gcsfs (>=2022.11.0)", "pandas-gbq (>=0.19.0)"] -hdf5 = ["tables (>=3.8.0)"] -html = ["beautifulsoup4 (>=4.11.2)", "html5lib (>=1.1)", "lxml (>=4.9.2)"] -mysql = ["SQLAlchemy (>=2.0.0)", "pymysql (>=1.0.2)"] -output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.9.0)"] -parquet = ["pyarrow (>=10.0.1)"] -performance = ["bottleneck (>=1.3.6)", "numba (>=0.56.4)", "numexpr (>=2.8.4)"] -plot = ["matplotlib (>=3.6.3)"] -postgresql = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "psycopg2 (>=2.9.6)"] -pyarrow = ["pyarrow (>=10.0.1)"] -spss = ["pyreadstat (>=1.2.0)"] -sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)"] -test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"] -xml = ["lxml (>=4.9.2)"] - -[[package]] -name = "pluggy" -version = "1.5.0" -description = "plugin and hook calling mechanisms for python" -optional = false -python-versions = ">=3.8" -files = [ - {file = "pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669"}, - {file = "pluggy-1.5.0.tar.gz", hash = "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1"}, -] - -[package.extras] -dev = ["pre-commit", "tox"] -testing = ["pytest", "pytest-benchmark"] - -[[package]] -name = "pytest" -version = "8.3.5" -description = "pytest: simple powerful testing with Python" -optional = false -python-versions = ">=3.8" -files = [ - {file = "pytest-8.3.5-py3-none-any.whl", hash = "sha256:c69214aa47deac29fad6c2a4f590b9c4a9fdb16a403176fe154b79c0b4d4d820"}, - {file = "pytest-8.3.5.tar.gz", hash = "sha256:f4efe70cc14e511565ac476b57c279e12a855b11f48f212af1080ef2263d3845"}, -] - -[package.dependencies] -colorama = {version = "*", markers = "sys_platform == \"win32\""} -iniconfig = "*" -packaging = "*" -pluggy = ">=1.5,<2" - -[package.extras] -dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"] - -[[package]] -name = "python-dateutil" -version = "2.9.0.post0" -description = "Extensions to the standard Python datetime module" -optional = false -python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" -files = [ - {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"}, - {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"}, -] - -[package.dependencies] -six = ">=1.5" - -[[package]] -name = "pytz" -version = "2025.1" -description = "World timezone definitions, modern and historical" -optional = false -python-versions = "*" -files = [ - {file = "pytz-2025.1-py2.py3-none-any.whl", hash = "sha256:89dd22dca55b46eac6eda23b2d72721bf1bdfef212645d81513ef5d03038de57"}, - {file = "pytz-2025.1.tar.gz", hash = "sha256:c2db42be2a2518b28e65f9207c4d05e6ff547d1efa4086469ef855e4ab70178e"}, -] - -[[package]] -name = "ruff" -version = "0.2.2" -description = "An extremely fast Python linter and code formatter, written in Rust." -optional = false -python-versions = ">=3.7" -files = [ - {file = "ruff-0.2.2-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:0a9efb032855ffb3c21f6405751d5e147b0c6b631e3ca3f6b20f917572b97eb6"}, - {file = "ruff-0.2.2-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:d450b7fbff85913f866a5384d8912710936e2b96da74541c82c1b458472ddb39"}, - {file = "ruff-0.2.2-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ecd46e3106850a5c26aee114e562c329f9a1fbe9e4821b008c4404f64ff9ce73"}, - {file = "ruff-0.2.2-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5e22676a5b875bd72acd3d11d5fa9075d3a5f53b877fe7b4793e4673499318ba"}, - {file = "ruff-0.2.2-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1695700d1e25a99d28f7a1636d85bafcc5030bba9d0578c0781ba1790dbcf51c"}, - {file = "ruff-0.2.2-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:b0c232af3d0bd8f521806223723456ffebf8e323bd1e4e82b0befb20ba18388e"}, - {file = "ruff-0.2.2-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f63d96494eeec2fc70d909393bcd76c69f35334cdbd9e20d089fb3f0640216ca"}, - {file = "ruff-0.2.2-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6a61ea0ff048e06de273b2e45bd72629f470f5da8f71daf09fe481278b175001"}, - {file = "ruff-0.2.2-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5e1439c8f407e4f356470e54cdecdca1bd5439a0673792dbe34a2b0a551a2fe3"}, - {file = "ruff-0.2.2-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:940de32dc8853eba0f67f7198b3e79bc6ba95c2edbfdfac2144c8235114d6726"}, - {file = "ruff-0.2.2-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:0c126da55c38dd917621552ab430213bdb3273bb10ddb67bc4b761989210eb6e"}, - {file = "ruff-0.2.2-py3-none-musllinux_1_2_i686.whl", hash = "sha256:3b65494f7e4bed2e74110dac1f0d17dc8e1f42faaa784e7c58a98e335ec83d7e"}, - {file = "ruff-0.2.2-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:1ec49be4fe6ddac0503833f3ed8930528e26d1e60ad35c2446da372d16651ce9"}, - {file = "ruff-0.2.2-py3-none-win32.whl", hash = "sha256:d920499b576f6c68295bc04e7b17b6544d9d05f196bb3aac4358792ef6f34325"}, - {file = "ruff-0.2.2-py3-none-win_amd64.whl", hash = "sha256:cc9a91ae137d687f43a44c900e5d95e9617cb37d4c989e462980ba27039d239d"}, - {file = "ruff-0.2.2-py3-none-win_arm64.whl", hash = "sha256:c9d15fc41e6054bfc7200478720570078f0b41c9ae4f010bcc16bd6f4d1aacdd"}, - {file = "ruff-0.2.2.tar.gz", hash = "sha256:e62ed7f36b3068a30ba39193a14274cd706bc486fad521276458022f7bccb31d"}, -] - -[[package]] -name = "scikit-learn" -version = "1.6.1" -description = "A set of python modules for machine learning and data mining" -optional = false -python-versions = ">=3.9" -files = [ - {file = "scikit_learn-1.6.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d056391530ccd1e501056160e3c9673b4da4805eb67eb2bdf4e983e1f9c9204e"}, - {file = "scikit_learn-1.6.1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:0c8d036eb937dbb568c6242fa598d551d88fb4399c0344d95c001980ec1c7d36"}, - {file = "scikit_learn-1.6.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8634c4bd21a2a813e0a7e3900464e6d593162a29dd35d25bdf0103b3fce60ed5"}, - {file = "scikit_learn-1.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:775da975a471c4f6f467725dff0ced5c7ac7bda5e9316b260225b48475279a1b"}, - {file = "scikit_learn-1.6.1-cp310-cp310-win_amd64.whl", hash = "sha256:8a600c31592bd7dab31e1c61b9bbd6dea1b3433e67d264d17ce1017dbdce8002"}, - {file = "scikit_learn-1.6.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:72abc587c75234935e97d09aa4913a82f7b03ee0b74111dcc2881cba3c5a7b33"}, - {file = "scikit_learn-1.6.1-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:b3b00cdc8f1317b5f33191df1386c0befd16625f49d979fe77a8d44cae82410d"}, - {file = "scikit_learn-1.6.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dc4765af3386811c3ca21638f63b9cf5ecf66261cc4815c1db3f1e7dc7b79db2"}, - {file = "scikit_learn-1.6.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:25fc636bdaf1cc2f4a124a116312d837148b5e10872147bdaf4887926b8c03d8"}, - {file = "scikit_learn-1.6.1-cp311-cp311-win_amd64.whl", hash = "sha256:fa909b1a36e000a03c382aade0bd2063fd5680ff8b8e501660c0f59f021a6415"}, - {file = "scikit_learn-1.6.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:926f207c804104677af4857b2c609940b743d04c4c35ce0ddc8ff4f053cddc1b"}, - {file = "scikit_learn-1.6.1-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:2c2cae262064e6a9b77eee1c8e768fc46aa0b8338c6a8297b9b6759720ec0ff2"}, - {file = "scikit_learn-1.6.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1061b7c028a8663fb9a1a1baf9317b64a257fcb036dae5c8752b2abef31d136f"}, - {file = "scikit_learn-1.6.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2e69fab4ebfc9c9b580a7a80111b43d214ab06250f8a7ef590a4edf72464dd86"}, - {file = "scikit_learn-1.6.1-cp312-cp312-win_amd64.whl", hash = "sha256:70b1d7e85b1c96383f872a519b3375f92f14731e279a7b4c6cfd650cf5dffc52"}, - {file = "scikit_learn-1.6.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:2ffa1e9e25b3d93990e74a4be2c2fc61ee5af85811562f1288d5d055880c4322"}, - {file = "scikit_learn-1.6.1-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:dc5cf3d68c5a20ad6d571584c0750ec641cc46aeef1c1507be51300e6003a7e1"}, - {file = "scikit_learn-1.6.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c06beb2e839ecc641366000ca84f3cf6fa9faa1777e29cf0c04be6e4d096a348"}, - {file = "scikit_learn-1.6.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e8ca8cb270fee8f1f76fa9bfd5c3507d60c6438bbee5687f81042e2bb98e5a97"}, - {file = "scikit_learn-1.6.1-cp313-cp313-win_amd64.whl", hash = "sha256:7a1c43c8ec9fde528d664d947dc4c0789be4077a3647f232869f41d9bf50e0fb"}, - {file = "scikit_learn-1.6.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:a17c1dea1d56dcda2fac315712f3651a1fea86565b64b48fa1bc090249cbf236"}, - {file = "scikit_learn-1.6.1-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:6a7aa5f9908f0f28f4edaa6963c0a6183f1911e63a69aa03782f0d924c830a35"}, - {file = "scikit_learn-1.6.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0650e730afb87402baa88afbf31c07b84c98272622aaba002559b614600ca691"}, - {file = "scikit_learn-1.6.1-cp313-cp313t-win_amd64.whl", hash = "sha256:3f59fe08dc03ea158605170eb52b22a105f238a5d512c4470ddeca71feae8e5f"}, - {file = "scikit_learn-1.6.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:6849dd3234e87f55dce1db34c89a810b489ead832aaf4d4550b7ea85628be6c1"}, - {file = "scikit_learn-1.6.1-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:e7be3fa5d2eb9be7d77c3734ff1d599151bb523674be9b834e8da6abe132f44e"}, - {file = "scikit_learn-1.6.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:44a17798172df1d3c1065e8fcf9019183f06c87609b49a124ebdf57ae6cb0107"}, - {file = "scikit_learn-1.6.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b8b7a3b86e411e4bce21186e1c180d792f3d99223dcfa3b4f597ecc92fa1a422"}, - {file = "scikit_learn-1.6.1-cp39-cp39-win_amd64.whl", hash = "sha256:7a73d457070e3318e32bdb3aa79a8d990474f19035464dfd8bede2883ab5dc3b"}, - {file = "scikit_learn-1.6.1.tar.gz", hash = "sha256:b4fc2525eca2c69a59260f583c56a7557c6ccdf8deafdba6e060f94c1c59738e"}, -] - -[package.dependencies] -joblib = ">=1.2.0" -numpy = ">=1.19.5" -scipy = ">=1.6.0" -threadpoolctl = ">=3.1.0" - -[package.extras] -benchmark = ["matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "pandas (>=1.1.5)"] -build = ["cython (>=3.0.10)", "meson-python (>=0.16.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)"] -docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "polars (>=0.20.30)", "pooch (>=1.6.0)", "pydata-sphinx-theme (>=0.15.3)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)", "sphinx (>=7.3.7)", "sphinx-copybutton (>=0.5.2)", "sphinx-design (>=0.5.0)", "sphinx-design (>=0.6.0)", "sphinx-gallery (>=0.17.1)", "sphinx-prompt (>=1.4.0)", "sphinx-remove-toctrees (>=1.0.0.post1)", "sphinxcontrib-sass (>=0.3.4)", "sphinxext-opengraph (>=0.9.1)", "towncrier (>=24.8.0)"] -examples = ["matplotlib (>=3.3.4)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)"] -install = ["joblib (>=1.2.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)", "threadpoolctl (>=3.1.0)"] -maintenance = ["conda-lock (==2.5.6)"] -tests = ["black (>=24.3.0)", "matplotlib (>=3.3.4)", "mypy (>=1.9)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "polars (>=0.20.30)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pyarrow (>=12.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.5.1)", "scikit-image (>=0.17.2)"] - -[[package]] -name = "scipy" -version = "1.15.2" -description = "Fundamental algorithms for scientific computing in Python" -optional = false -python-versions = ">=3.10" -files = [ - {file = "scipy-1.15.2-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:a2ec871edaa863e8213ea5df811cd600734f6400b4af272e1c011e69401218e9"}, - {file = "scipy-1.15.2-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:6f223753c6ea76983af380787611ae1291e3ceb23917393079dcc746ba60cfb5"}, - {file = "scipy-1.15.2-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:ecf797d2d798cf7c838c6d98321061eb3e72a74710e6c40540f0e8087e3b499e"}, - {file = "scipy-1.15.2-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:9b18aa747da280664642997e65aab1dd19d0c3d17068a04b3fe34e2559196cb9"}, - {file = "scipy-1.15.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:87994da02e73549dfecaed9e09a4f9d58a045a053865679aeb8d6d43747d4df3"}, - {file = "scipy-1.15.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:69ea6e56d00977f355c0f84eba69877b6df084516c602d93a33812aa04d90a3d"}, - {file = "scipy-1.15.2-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:888307125ea0c4466287191e5606a2c910963405ce9671448ff9c81c53f85f58"}, - {file = "scipy-1.15.2-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:9412f5e408b397ff5641080ed1e798623dbe1ec0d78e72c9eca8992976fa65aa"}, - {file = "scipy-1.15.2-cp310-cp310-win_amd64.whl", hash = "sha256:b5e025e903b4f166ea03b109bb241355b9c42c279ea694d8864d033727205e65"}, - {file = "scipy-1.15.2-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:92233b2df6938147be6fa8824b8136f29a18f016ecde986666be5f4d686a91a4"}, - {file = "scipy-1.15.2-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:62ca1ff3eb513e09ed17a5736929429189adf16d2d740f44e53270cc800ecff1"}, - {file = "scipy-1.15.2-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:4c6676490ad76d1c2894d77f976144b41bd1a4052107902238047fb6a473e971"}, - {file = "scipy-1.15.2-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:a8bf5cb4a25046ac61d38f8d3c3426ec11ebc350246a4642f2f315fe95bda655"}, - {file = "scipy-1.15.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6a8e34cf4c188b6dd004654f88586d78f95639e48a25dfae9c5e34a6dc34547e"}, - {file = "scipy-1.15.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:28a0d2c2075946346e4408b211240764759e0fabaeb08d871639b5f3b1aca8a0"}, - {file = "scipy-1.15.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:42dabaaa798e987c425ed76062794e93a243be8f0f20fff6e7a89f4d61cb3d40"}, - {file = "scipy-1.15.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:6f5e296ec63c5da6ba6fa0343ea73fd51b8b3e1a300b0a8cae3ed4b1122c7462"}, - {file = "scipy-1.15.2-cp311-cp311-win_amd64.whl", hash = "sha256:597a0c7008b21c035831c39927406c6181bcf8f60a73f36219b69d010aa04737"}, - {file = "scipy-1.15.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:c4697a10da8f8765bb7c83e24a470da5797e37041edfd77fd95ba3811a47c4fd"}, - {file = "scipy-1.15.2-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:869269b767d5ee7ea6991ed7e22b3ca1f22de73ab9a49c44bad338b725603301"}, - {file = "scipy-1.15.2-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:bad78d580270a4d32470563ea86c6590b465cb98f83d760ff5b0990cb5518a93"}, - {file = "scipy-1.15.2-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:b09ae80010f52efddb15551025f9016c910296cf70adbf03ce2a8704f3a5ad20"}, - {file = "scipy-1.15.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5a6fd6eac1ce74a9f77a7fc724080d507c5812d61e72bd5e4c489b042455865e"}, - {file = "scipy-1.15.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2b871df1fe1a3ba85d90e22742b93584f8d2b8e6124f8372ab15c71b73e428b8"}, - {file = "scipy-1.15.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:03205d57a28e18dfd39f0377d5002725bf1f19a46f444108c29bdb246b6c8a11"}, - {file = "scipy-1.15.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:601881dfb761311045b03114c5fe718a12634e5608c3b403737ae463c9885d53"}, - {file = "scipy-1.15.2-cp312-cp312-win_amd64.whl", hash = "sha256:e7c68b6a43259ba0aab737237876e5c2c549a031ddb7abc28c7b47f22e202ded"}, - {file = "scipy-1.15.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:01edfac9f0798ad6b46d9c4c9ca0e0ad23dbf0b1eb70e96adb9fa7f525eff0bf"}, - {file = "scipy-1.15.2-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:08b57a9336b8e79b305a143c3655cc5bdbe6d5ece3378578888d2afbb51c4e37"}, - {file = "scipy-1.15.2-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:54c462098484e7466362a9f1672d20888f724911a74c22ae35b61f9c5919183d"}, - {file = "scipy-1.15.2-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:cf72ff559a53a6a6d77bd8eefd12a17995ffa44ad86c77a5df96f533d4e6c6bb"}, - {file = "scipy-1.15.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9de9d1416b3d9e7df9923ab23cd2fe714244af10b763975bea9e4f2e81cebd27"}, - {file = "scipy-1.15.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fb530e4794fc8ea76a4a21ccb67dea33e5e0e60f07fc38a49e821e1eae3b71a0"}, - {file = "scipy-1.15.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5ea7ed46d437fc52350b028b1d44e002646e28f3e8ddc714011aaf87330f2f32"}, - {file = "scipy-1.15.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:11e7ad32cf184b74380f43d3c0a706f49358b904fa7d5345f16ddf993609184d"}, - {file = "scipy-1.15.2-cp313-cp313-win_amd64.whl", hash = "sha256:a5080a79dfb9b78b768cebf3c9dcbc7b665c5875793569f48bf0e2b1d7f68f6f"}, - {file = "scipy-1.15.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:447ce30cee6a9d5d1379087c9e474628dab3db4a67484be1b7dc3196bfb2fac9"}, - {file = "scipy-1.15.2-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:c90ebe8aaa4397eaefa8455a8182b164a6cc1d59ad53f79943f266d99f68687f"}, - {file = "scipy-1.15.2-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:def751dd08243934c884a3221156d63e15234a3155cf25978b0a668409d45eb6"}, - {file = "scipy-1.15.2-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:302093e7dfb120e55515936cb55618ee0b895f8bcaf18ff81eca086c17bd80af"}, - {file = "scipy-1.15.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7cd5b77413e1855351cdde594eca99c1f4a588c2d63711388b6a1f1c01f62274"}, - {file = "scipy-1.15.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d0194c37037707b2afa7a2f2a924cf7bac3dc292d51b6a925e5fcb89bc5c776"}, - {file = "scipy-1.15.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:bae43364d600fdc3ac327db99659dcb79e6e7ecd279a75fe1266669d9a652828"}, - {file = "scipy-1.15.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:f031846580d9acccd0044efd1a90e6f4df3a6e12b4b6bd694a7bc03a89892b28"}, - {file = "scipy-1.15.2-cp313-cp313t-win_amd64.whl", hash = "sha256:fe8a9eb875d430d81755472c5ba75e84acc980e4a8f6204d402849234d3017db"}, - {file = "scipy-1.15.2.tar.gz", hash = "sha256:cd58a314d92838f7e6f755c8a2167ead4f27e1fd5c1251fd54289569ef3495ec"}, -] - -[package.dependencies] -numpy = ">=1.23.5,<2.5" - -[package.extras] -dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"] -doc = ["intersphinx_registry", "jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.16.5)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<8.0.0)", "sphinx-copybutton", "sphinx-design (>=0.4.0)"] -test = ["Cython", "array-api-strict (>=2.0,<2.1.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] - -[[package]] -name = "six" -version = "1.17.0" -description = "Python 2 and 3 compatibility utilities" -optional = false -python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" -files = [ - {file = "six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274"}, - {file = "six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"}, -] - -[[package]] -name = "threadpoolctl" -version = "3.5.0" -description = "threadpoolctl" -optional = false -python-versions = ">=3.8" -files = [ - {file = "threadpoolctl-3.5.0-py3-none-any.whl", hash = "sha256:56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467"}, - {file = "threadpoolctl-3.5.0.tar.gz", hash = "sha256:082433502dd922bf738de0d8bcc4fdcbf0979ff44c42bd40f5af8a282f6fa107"}, -] - -[[package]] -name = "tzdata" -version = "2025.1" -description = "Provider of IANA time zone data" -optional = false -python-versions = ">=2" -files = [ - {file = "tzdata-2025.1-py2.py3-none-any.whl", hash = "sha256:7e127113816800496f027041c570f50bcd464a020098a3b6b199517772303639"}, - {file = "tzdata-2025.1.tar.gz", hash = "sha256:24894909e88cdb28bd1636c6887801df64cb485bd593f2fd83ef29075a81d694"}, -] - -[metadata] -lock-version = "2.0" -python-versions = "^3.11" -content-hash = "8deba4f2f65ebce004129edd41a6ab9792fd26cf058aedb9880a24545cb92659" diff --git a/pyproject.toml b/pyproject.toml index 4c63dda..f0e6e4e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,29 +1,134 @@ -[tool.poetry] +[project] name = "unsupervised-bias-detection" -version = "0.2.6" -description = "package for unsupervised bias detection" -authors = ["NGO Algorithm Audit"] -license = "EUPL-1.2 license" +version = "1.0.0" +description = "Unsupervised detection of bias in data." readme = "README.md" +requires-python = ">=3.11" +license = "EUPL-1.2 license" +authors = [ + {name = "NGO Algorithm Audit", email = "info@algorithmaudit.eu"} +] +dependencies = [ + "kmodes>=0.12.2", + "numpy>=2.4.3", + "scikit-learn>=1.8.0", +] + +[project.urls] +"Repository" = "https://github.com/NGO-Algorithm-Audit/unsupervised-bias-detection" + +[dependency-groups] +dev = [ + "fairlearn>=0.13.0", + "pandas>=3.0.1", + "pytest>=9.0.2", + "ruff>=0.15.5", +] -[tool.poetry.dependencies] -python = "^3.11" -numpy = "^1.26.4" -scikit-learn = ">=1.6.1" -kmodes = "^0.12.2" +[tool.pytest.ini_options] +testpaths = ["tests", "unsupervised_bias_detection"] +addopts = "--verbose --showlocals --full-trace --color=yes --doctest-modules" -[tool.poetry.group.dev.dependencies] -ruff = "^0.2.2" -pytest = "^8.0.2" -pandas = "^2.2.2" -fairlearn = "^0.10.0" +[tool.ruff] +# Set the maximum line length to 88 (Black's default) +line-length = 88 + +# Assume Python 3.12+ +target-version = "py312" + +# Exclude common directories +exclude = [ + ".bzr", + ".direnv", + ".eggs", + ".git", + ".git-rewrite", + ".hg", + ".ipynb_checkpoints", + ".mypy_cache", + ".nox", + ".pants.d", + ".pytype", + ".ruff_cache", + ".svn", + ".tox", + ".venv", + "__pypackages__", + "__pycache__", + "_build", + "buck-out", + "build", + "dist", + "node_modules", + "venv", +] [tool.ruff.lint] -select = ["D"] +# Enable these rule categories +select = [ + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # pyflakes + "I", # isort + "B", # flake8-bugbear + "C4", # flake8-comprehensions + "UP", # pyupgrade + "ARG", # flake8-unused-arguments + "SIM", # flake8-simplify + "NPY", # NumPy-specific rules + "PD", # pandas-vet + "RUF", # Ruff-specific rules + "D", # pydocstyle (docstring conventions) +] + +# Disable specific rules if needed +ignore = [ + "D202", # no blank lines allowed after docstring + "D203", # 1 blank line required before class docstring (conflicts with D211) + "D212", # multi-line docstring summary should start at first line (conflicts with D213) +] + +# Allow autofix for all enabled rules (when `--fix` is provided) +fixable = ["ALL"] +unfixable = [] + +# Allow unused variables when underscore-prefixed +dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" [tool.ruff.lint.pydocstyle] convention = "numpy" -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" +[tool.ruff.lint.per-file-ignores] +# Ignore specific rules in specific files +"__init__.py" = ["F401"] # unused imports in __init__.py +"tests/**/*.py" = [ + "S101", # use of assert detected + "ARG", # unused function arguments + "FBT", # boolean trap +] + +[tool.ruff.lint.isort] +# Configure import sorting +known-first-party = ["unsupervised_bias_detection"] +force-sort-within-sections = true +split-on-trailing-comma = false + +[tool.ruff.lint.mccabe] +# Set complexity threshold +max-complexity = 10 + +[tool.ruff.format] +# Use double quotes for strings +quote-style = "double" + +# Indent with spaces +indent-style = "space" + +# Respect magic trailing commas +skip-magic-trailing-comma = false + +# Auto-format docstrings +docstring-code-format = true + +# Line ending style +line-ending = "auto" diff --git a/tests/__init__.py b/tests/__init__.py index e69de29..cf2988d 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for the unsupervised_bias_detection package.""" diff --git a/tests/test_bahc.py b/tests/test_bahc.py deleted file mode 100644 index 187fa39..0000000 --- a/tests/test_bahc.py +++ /dev/null @@ -1,79 +0,0 @@ -import numpy as np -from unsupervised_bias_detection.cluster import BiasAwareHierarchicalKMeans - - -def test_shapes(): - # Checks that labels and scores have the right shapes - rng = np.random.RandomState(12) - X = rng.rand(20, 10) - y = rng.rand(20) - bahc = BiasAwareHierarchicalKMeans(bahc_max_iter=5, bahc_min_cluster_size=2) - bahc.fit(X, y) - assert len(bahc.labels_) == len(X) - assert len(bahc.scores_) == bahc.n_clusters_ - - -def test_labels(): - # Checks that label values are between 0 and n_clusters - rng = np.random.RandomState(12) - X = rng.rand(20, 10) - y = rng.rand(20) - bahc = BiasAwareHierarchicalKMeans(bahc_max_iter=5, bahc_min_cluster_size=2) - bahc.fit(X, y) - assert np.array_equal(np.unique(bahc.labels_), np.arange(bahc.n_clusters_)) - - -def test_cluster_sizes(): - # Checks that cluster sizes are at least bahc_min_cluster_size - rng = np.random.RandomState(12) - X = rng.rand(20, 10) - y = rng.rand(20) - bahc = BiasAwareHierarchicalKMeans(bahc_max_iter=5, bahc_min_cluster_size=5) - bahc.fit(X, y) - assert np.all(np.bincount(bahc.labels_) >= bahc.bahc_min_cluster_size) - - -def test_constant_metric(): - # Checks that there is only one cluster with a score of 0 if the metric is constant - rng = np.random.RandomState(12) - X = rng.rand(20, 10) - y = np.full(20, rng.rand()) - bahc = BiasAwareHierarchicalKMeans(bahc_max_iter=5, bahc_min_cluster_size=2) - bahc.fit(X, y) - assert bahc.n_clusters_ == 1 - assert bahc.scores_[0] == 0 - - -def test_scores(): - # Checks that scores are computed correctly - rng = np.random.RandomState(12) - X = rng.rand(20, 10) - y = rng.rand(20) - bahc = BiasAwareHierarchicalKMeans(bahc_max_iter=5, bahc_min_cluster_size=2) - bahc.fit(X, y) - # TODO: Check this!!! - for i in range(bahc.n_clusters_): - cluster_indices = np.arange(20)[bahc.labels_ == i] - complement_indices = np.arange(20)[bahc.labels_ != i] - score = np.mean(y[complement_indices]) - np.mean(y[cluster_indices]) - assert bahc.scores_[i] == score - - -def test_scores_are_sorted(): - # Checks that scores are sorted in descending order - rng = np.random.RandomState(12) - X = rng.rand(20, 10) - y = rng.rand(20) - bahc = BiasAwareHierarchicalKMeans(bahc_max_iter=5, bahc_min_cluster_size=2) - bahc.fit(X, y) - assert np.all(bahc.scores_[:-1] >= bahc.scores_[1:]) - - -def test_predict(): - # Checks that predict returns the same labels as fit - rng = np.random.RandomState(12) - X = rng.rand(20, 10) - y = rng.rand(20) - bahc = BiasAwareHierarchicalKMeans(bahc_max_iter=5, bahc_min_cluster_size=2) - bahc.fit(X, y) - assert np.array_equal(bahc.predict(X), bahc.labels_) diff --git a/tests/test_categorical.py b/tests/test_categorical.py new file mode 100644 index 0000000..c7d7d89 --- /dev/null +++ b/tests/test_categorical.py @@ -0,0 +1,113 @@ +"""Tests for categorical bias-aware hierarchical clustering algorithms.""" + +import numpy as np +import pytest + +from unsupervised_bias_detection.cluster import ( + BiasAwareHierarchicalClustering, + BiasAwareHierarchicalKModes, +) + +CATEGORICAL_ALGORITHMS = [BiasAwareHierarchicalKModes] +CATEGORICAL_ALGORITHMS_WITH_PREDICT = [BiasAwareHierarchicalKModes] + + +@pytest.fixture +def data(): + """Generate random data with categorical features for testing.""" + rng = np.random.RandomState(12) + X = rng.randint(0, 5, size=(20, 10)) + y = rng.rand(20) + return X, y + + +@pytest.mark.parametrize("algorithm", CATEGORICAL_ALGORITHMS, ids=lambda a: a.__name__) +def test_shapes(algorithm: type[BiasAwareHierarchicalClustering], data): + """Test that labels and scores have the right shapes.""" + X, y = data + bahc = algorithm(bahc_max_iter=5, bahc_min_cluster_size=2) + bahc.fit(X, y) + assert len(bahc.labels_) == len( + X + ), f"labels_ length {len(bahc.labels_)} does not match X length {len(X)}" + assert len(bahc.scores_) == bahc.n_clusters_, ( + f"scores_ length {len(bahc.scores_)} " + f"does not match n_clusters_ {bahc.n_clusters_}" + ) + + +@pytest.mark.parametrize("algorithm", CATEGORICAL_ALGORITHMS, ids=lambda a: a.__name__) +def test_labels(algorithm: type[BiasAwareHierarchicalClustering], data): + """Test that unique labels are np.arange(n_clusters_).""" + X, y = data + bahc = algorithm(bahc_max_iter=4, bahc_min_cluster_size=2) + bahc.fit(X, y) + unique_labels = np.unique(bahc.labels_) + assert np.array_equal(unique_labels, np.arange(bahc.n_clusters_)), ( + f"Unique labels {unique_labels} do not match " + f"expected range {np.arange(bahc.n_clusters_)} " + f"for n_clusters_={bahc.n_clusters_}" + ) + + +@pytest.mark.parametrize("algorithm", CATEGORICAL_ALGORITHMS, ids=lambda a: a.__name__) +def test_cluster_sizes(algorithm: type[BiasAwareHierarchicalClustering], data): + """Test that all clusters have at least bahc_min_cluster_size samples.""" + X, y = data + min_cluster_size = 5 + model = algorithm(bahc_max_iter=5, bahc_min_cluster_size=min_cluster_size) + model.fit(X, y) + sizes = np.bincount(model.labels_) + assert np.all( + sizes >= min_cluster_size + ), f"Cluster sizes found: {sizes}, expected each to be >= {min_cluster_size}" + + +@pytest.mark.parametrize("algorithm", CATEGORICAL_ALGORITHMS, ids=lambda a: a.__name__) +def test_constant_metric(algorithm: type[BiasAwareHierarchicalClustering]): + """Test that there is only one cluster with a score of 0 for constant metric.""" + rng = np.random.RandomState(12) + X = rng.randint(0, 5, size=(20, 10)) + y = np.full(20, rng.rand()) + model = algorithm(bahc_max_iter=5, bahc_min_cluster_size=2) + model.fit(X, y) + assert model.n_clusters_ == 1, f"Expected 1 cluster, found {model.n_clusters_}" + assert model.scores_[0] == 0, f"Expected score of 0, found {model.scores_[0]}" + + +@pytest.mark.parametrize("algorithm", CATEGORICAL_ALGORITHMS, ids=lambda a: a.__name__) +def test_scores(algorithm: type[BiasAwareHierarchicalClustering], data): + """Test that scores are computed correctly.""" + X, y = data + model = algorithm(bahc_max_iter=5, bahc_min_cluster_size=2) + model.fit(X, y) + # TODO: Check this!!! + for i in range(model.n_clusters_): + cluster_indices = np.arange(20)[model.labels_ == i] + complement_indices = np.arange(20)[model.labels_ != i] + score = np.mean(y[complement_indices]) - np.mean(y[cluster_indices]) + assert model.scores_[i] == score + + +@pytest.mark.parametrize("algorithm", CATEGORICAL_ALGORITHMS, ids=lambda a: a.__name__) +def test_scores_are_sorted(algorithm: type[BiasAwareHierarchicalClustering], data): + """Test that scores are sorted in descending order.""" + X, y = data + model = algorithm(bahc_max_iter=5, bahc_min_cluster_size=2) + model.fit(X, y) + assert np.all( + model.scores_[:-1] >= model.scores_[1:] + ), "Scores are not sorted in descending order" + + +@pytest.mark.parametrize( + "algorithm", CATEGORICAL_ALGORITHMS_WITH_PREDICT, ids=lambda a: a.__name__ +) +def test_predict(algorithm: type[BiasAwareHierarchicalClustering], data): + """Test that predict returns the same labels on the data used to fit the model.""" + X, y = data + model = algorithm(bahc_max_iter=5, bahc_min_cluster_size=2) + model.fit(X, y) + assert np.array_equal( + model.predict(X), model.labels_ + ), "Predict does not return the same labels on the data used to fit the model" diff --git a/tests/test_dataset.py b/tests/test_dataset.py index d2b2dbb..c62faa4 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -1,4 +1,4 @@ -"""Provides tests for the loading function in unsupervised_bias_detection/utils/dataset.py.""" +"""Tests for the loading function in unsupervised_bias_detection/utils/dataset.py.""" import pytest from unsupervised_bias_detection.utils.dataset import load_default_dataset diff --git a/tests/test_numerical.py b/tests/test_numerical.py new file mode 100644 index 0000000..8b82fce --- /dev/null +++ b/tests/test_numerical.py @@ -0,0 +1,113 @@ +"""Tests for numerical bias-aware hierarchical clustering algorithms.""" + +import numpy as np +import pytest + +from unsupervised_bias_detection.cluster import ( + BiasAwareHierarchicalClustering, + BiasAwareHierarchicalKMeans, +) + +NUMERICAL_ALGORITHMS = [BiasAwareHierarchicalKMeans] +NUMERICAL_ALGORITHMS_WITH_PREDICT = [BiasAwareHierarchicalKMeans] + + +@pytest.fixture +def data(): + """Generate random data with numerical features for testing.""" + rng = np.random.RandomState(12) + X = rng.rand(20, 10) + y = rng.rand(20) + return X, y + + +@pytest.mark.parametrize("algorithm", NUMERICAL_ALGORITHMS, ids=lambda a: a.__name__) +def test_shapes(algorithm: type[BiasAwareHierarchicalClustering], data): + """Test that labels and scores have the right shapes.""" + X, y = data + bahc = algorithm(bahc_max_iter=5, bahc_min_cluster_size=2) + bahc.fit(X, y) + assert len(bahc.labels_) == len( + X + ), f"labels_ length {len(bahc.labels_)} does not match X length {len(X)}" + assert len(bahc.scores_) == bahc.n_clusters_, ( + f"scores_ length {len(bahc.scores_)} " + f"does not match n_clusters_ {bahc.n_clusters_}" + ) + + +@pytest.mark.parametrize("algorithm", NUMERICAL_ALGORITHMS, ids=lambda a: a.__name__) +def test_labels(algorithm: type[BiasAwareHierarchicalClustering], data): + """Test that unique labels are np.arange(n_clusters_).""" + X, y = data + bahc = algorithm(bahc_max_iter=4, bahc_min_cluster_size=2) + bahc.fit(X, y) + unique_labels = np.unique(bahc.labels_) + assert np.array_equal(unique_labels, np.arange(bahc.n_clusters_)), ( + f"Unique labels {unique_labels} do not match " + f"expected range {np.arange(bahc.n_clusters_)} " + f"for n_clusters_={bahc.n_clusters_}" + ) + + +@pytest.mark.parametrize("algorithm", NUMERICAL_ALGORITHMS, ids=lambda a: a.__name__) +def test_cluster_sizes(algorithm: type[BiasAwareHierarchicalClustering], data): + """Test that all clusters have at least bahc_min_cluster_size samples.""" + X, y = data + min_cluster_size = 5 + model = algorithm(bahc_max_iter=5, bahc_min_cluster_size=min_cluster_size) + model.fit(X, y) + sizes = np.bincount(model.labels_) + assert np.all( + sizes >= min_cluster_size + ), f"Cluster sizes found: {sizes}, expected each to be >= {min_cluster_size}" + + +@pytest.mark.parametrize("algorithm", NUMERICAL_ALGORITHMS, ids=lambda a: a.__name__) +def test_constant_metric(algorithm: type[BiasAwareHierarchicalClustering]): + """Test that there is only one cluster with a score of 0 for constant metric.""" + rng = np.random.RandomState(12) + X = rng.rand(20, 10) + y = np.full(20, rng.rand()) + model = algorithm(bahc_max_iter=5, bahc_min_cluster_size=2) + model.fit(X, y) + assert model.n_clusters_ == 1, f"Expected 1 cluster, found {model.n_clusters_}" + assert model.scores_[0] == 0, f"Expected score of 0, found {model.scores_[0]}" + + +@pytest.mark.parametrize("algorithm", NUMERICAL_ALGORITHMS, ids=lambda a: a.__name__) +def test_scores(algorithm: type[BiasAwareHierarchicalClustering], data): + """Test that scores are computed correctly.""" + X, y = data + model = algorithm(bahc_max_iter=5, bahc_min_cluster_size=2) + model.fit(X, y) + # TODO: Check this!!! + for i in range(model.n_clusters_): + cluster_indices = np.arange(20)[model.labels_ == i] + complement_indices = np.arange(20)[model.labels_ != i] + score = np.mean(y[complement_indices]) - np.mean(y[cluster_indices]) + assert model.scores_[i] == score + + +@pytest.mark.parametrize("algorithm", NUMERICAL_ALGORITHMS, ids=lambda a: a.__name__) +def test_scores_are_sorted(algorithm: type[BiasAwareHierarchicalClustering], data): + """Test that scores are sorted in descending order.""" + X, y = data + model = algorithm(bahc_max_iter=5, bahc_min_cluster_size=2) + model.fit(X, y) + assert np.all( + model.scores_[:-1] >= model.scores_[1:] + ), "Scores are not sorted in descending order" + + +@pytest.mark.parametrize( + "algorithm", NUMERICAL_ALGORITHMS_WITH_PREDICT, ids=lambda a: a.__name__ +) +def test_predict(algorithm: type[BiasAwareHierarchicalClustering], data): + """Test that predict returns the same labels on the data used to fit the model.""" + X, y = data + model = algorithm(bahc_max_iter=5, bahc_min_cluster_size=2) + model.fit(X, y) + assert np.array_equal( + model.predict(X), model.labels_ + ), "Predict does not return the same labels on the data used to fit the model" diff --git a/tests/test_validation.py b/tests/test_validation.py index 0ff931d..cc62915 100644 --- a/tests/test_validation.py +++ b/tests/test_validation.py @@ -1,6 +1,6 @@ -"""Provides tests for the functions in unsupervised_bias_detection/utils/validation.py.""" -import pandas as pd +"""Tests for the functions in unsupervised_bias_detection/utils/validation.py.""" import numpy as np +import pandas as pd import pytest from unsupervised_bias_detection.utils.validation import run_checks @@ -14,7 +14,7 @@ def test_always_passes(): "true_labels": [0, 0, 1], } df_test0 = pd.DataFrame(data=dict0) - assert not run_checks(df_test0) is ValueError + assert run_checks(df_test0) is not ValueError @pytest.mark.xfail diff --git a/unsupervised_bias_detection/__init__.py b/unsupervised_bias_detection/__init__.py index c545b0c..4a4ca73 100644 --- a/unsupervised_bias_detection/__init__.py +++ b/unsupervised_bias_detection/__init__.py @@ -1 +1 @@ -"""unsupervised-bias-detection.""" \ No newline at end of file +"""unsupervised-bias-detection.""" diff --git a/unsupervised_bias_detection/cluster/__init__.py b/unsupervised_bias_detection/cluster/__init__.py index 7bcb391..8b61b48 100644 --- a/unsupervised_bias_detection/cluster/__init__.py +++ b/unsupervised_bias_detection/cluster/__init__.py @@ -1,4 +1,4 @@ -"""The :mod:`unsupervised_bias_detection.cluster` module implements bias-aware clustering algorithms.""" +"""Bias-aware hierarchical clustering algorithms.""" from ._bahc import BiasAwareHierarchicalClustering from ._kmeans import BiasAwareHierarchicalKMeans diff --git a/unsupervised_bias_detection/cluster/_bahc.py b/unsupervised_bias_detection/cluster/_bahc.py index bf859b2..a95a1b1 100644 --- a/unsupervised_bias_detection/cluster/_bahc.py +++ b/unsupervised_bias_detection/cluster/_bahc.py @@ -1,32 +1,97 @@ -from ._cluster_node import ClusterNode -from collections import deque import heapq -from numbers import Integral +from collections import deque +from numbers import Integral, Real +from typing import Any, ClassVar + import numpy as np from sklearn.base import BaseEstimator, ClusterMixin -from sklearn.utils._param_validation import Interval -from sklearn.utils.validation import validate_data -from typing import Any, Type +from sklearn.utils._param_validation import HasMethods, Interval +from sklearn.utils.validation import check_is_fitted, validate_data + +from ._cluster_node import ClusterNode class BiasAwareHierarchicalClustering(BaseEstimator, ClusterMixin): """ - TODO: Add docstring. + Bias-Aware Hierarchical Clustering. + + BiasAwareHierarchicalClustering performs hierarchical clustering in a way that + is aware of potential bias within the performance metric of interest. + In each iteration, the method takes the cluster with the highest standard deviation + of the performance metric among those clusters that were not taken in previous + iterations. It then uses the `clustering_cls` to split the selected cluster + into child clusters. The split is valid if the discrimination score of at least one + child cluster is greater than or equal to the current discrimination score plus + `margin` and all child clusters meet the minimum cluster size requirement. + The discrimination score of a cluster is the difference between the mean of the + performance metric on the complement of the cluster and the mean of the + performance metric on the cluster. The method stops when the maximum number of + iterations is reached or no more valid splits are possible. + + Parameters + ---------- + clustering_cls : Type[ClusterMixin] + The clustering class to use for each hierarchical split + (e.g., sklearn.cluster.KMeans). + bahc_max_iter : int + Maximum number of iterations to run the hierarchical splitting procedure. + bahc_min_cluster_size : int + The minimum size a cluster must have to be further split. + margin : float, optional (default=1e-5) + Minimum score improvement required to split a cluster. + **clustering_params : dict + Additional hyperparameters to pass to clustering_cls upon instantiation. + + Attributes + ---------- + n_clusters_ : int + The number of clusters found by the algorithm. + labels_ : ndarray of shape (n_samples,) + Cluster labels for each point. Lower labels correspond to + higher discrimination scores. + scores_ : ndarray of shape (n_clusters_,) + Discrimination scores for each cluster. + cluster_tree_ : ClusterNode + The root node of the cluster tree. References ---------- - .. [1] J. Misztal-Radecka, B. Indurkhya, "Bias-Aware Hierarchical Clustering for detecting the discriminated - groups of users in recommendation systems", Information Processing & Management, vol. 58, no. 3, May. 2021. + .. [1] J. Misztal-Radecka, B. Indurkhya, "Bias-Aware Hierarchical Clustering + for detecting the discriminated groups of users in recommendation systems", + Information Processing & Management, vol. 58, no. 3, May. 2021. + + Examples + -------- + >>> from unsupervised_bias_detection.cluster import ( + ... BiasAwareHierarchicalClustering, + ... ) + >>> import numpy as np + >>> from sklearn.cluster import KMeans + >>> X = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]]) + >>> y = np.array([0, 0, 0, 10, 10, 10]) + >>> bahc = BiasAwareHierarchicalClustering( + ... clustering_cls=KMeans, + ... bahc_max_iter=1, + ... bahc_min_cluster_size=1, + ... n_clusters=2, + ... random_state=12, + ... ).fit(X, y) + >>> bahc.labels_ + array([0, 0, 0, 1, 1, 1], dtype=uint32) + >>> bahc.scores_ + array([ 10., -10.]) """ - _parameter_constraints: dict = { + _parameter_constraints: ClassVar[dict] = { + "clustering_cls": [HasMethods(["fit"])], "bahc_max_iter": [Interval(Integral, 1, None, closed="left")], "bahc_min_cluster_size": [Interval(Integral, 1, None, closed="left")], + "margin": [Interval(Real, 0, None, closed="left")], } def __init__( self, - clustering_cls: Type[ClusterMixin], + clustering_cls: type[ClusterMixin], bahc_max_iter: int, bahc_min_cluster_size: int, margin: float = 1e-5, @@ -47,20 +112,26 @@ def fit(self, X, y): List of n_features-dimensional data points. Each row corresponds to a single data point. y : array-like of shape (n_samples) - Metric values. + Performance metric values. Returns ------- self : object Fitted estimator. """ + self._validate_params() + if not issubclass(self.clustering_cls, BaseEstimator): + raise TypeError( + f"clustering_cls must derive from BaseEstimator, " + f"got {self.clustering_cls.__name__}" + ) + if not issubclass(self.clustering_cls, ClusterMixin): + raise TypeError( + f"clustering_cls must derive from ClusterMixin, " + f"got {self.clustering_cls.__name__}" + ) X, y = validate_data( - self, - X, - y, - reset=False, - accept_large_sparse=False, - order="C", + self, X, y, reset=True, accept_large_sparse=False, order="C" ) n_samples, _ = X.shape # We start with all samples being in a single cluster with label 0 @@ -68,9 +139,9 @@ def fit(self, X, y): labels = np.zeros(n_samples, dtype=np.uint32) leaves = [] label = 0 - std = np.std(y) # The entire dataset has a discrimination score of zero score = 0 + std = np.std(y) root = ClusterNode(label, score) self.cluster_tree_ = root heap = [(-std, label, root)] @@ -91,7 +162,7 @@ def fit(self, X, y): n_children = clustering_model.n_clusters_ else: n_children = len(np.unique(cluster_labels)) - + # We first check if all child clusters meet the minimum size requirement valid_split = True children_indices = [] @@ -102,8 +173,10 @@ def fit(self, X, y): else: valid_split = False break - - # If all children clusters are of sufficient size, we check if the score of any child cluster is greater than or equal to the current score + + # If all child clusters are of sufficient size, + # we check if the score of any child cluster is + # greater than or equal to the current score if valid_split: valid_split = False child_scores = [] @@ -116,15 +189,15 @@ def fit(self, X, y): if child_score >= score + self.margin: valid_split = True child_scores.append(child_score) - - # If the split is valid, we create the children nodes and split the current node - # Otherwise, we add the current node to the leaves + if valid_split: + # If the split is valid, + # we create the child nodes and split the current node first_child_indices = children_indices[0] first_child_score = child_scores[0] first_child = ClusterNode(label, first_child_score) first_child_std = np.std(y[first_child_indices]) - # heapq implements min-heap, so we negate std before pushing + # heapq implements a min-heap, so we negate std before pushing heapq.heappush(heap, (-first_child_std, label, first_child)) labels[first_child_indices] = label children = [first_child] @@ -139,8 +212,9 @@ def fit(self, X, y): self.n_clusters_ += 1 node.split(clustering_model, children) else: + # Otherwise, we add the current node to the leaves leaves.append(node) - + leaves.extend([leaf for _, _, leaf in heap]) leaf_scores = np.array([leaf.score for leaf in leaves]) # We sort clusters by decreasing scores @@ -154,7 +228,7 @@ def fit(self, X, y): for leaf in leaves: leaf.label = label_mapping[leaf.label] return self - + def predict(self, X): """Predict the cluster labels for the given data. @@ -162,10 +236,14 @@ def predict(self, X): ---------- X : array-like of shape (n_samples, n_features) """ - # TODO: Assert that fit has been called - # TODO: Assert that X has the same number of features as the data used to fit - # TODO: Assert that clustering_model has predict method - # TODO: Validate X + # Check if the clustering class has predict method + if not hasattr(self.clustering_cls, "predict"): + raise AttributeError( + f"clustering_cls {self.clustering_cls.__name__} " + f"does not have a predict method." + ) + check_is_fitted(self, attributes="cluster_tree_") + X = validate_data(self, X, reset=False, accept_large_sparse=False, order="C") n_samples, _ = X.shape labels = np.zeros(n_samples, dtype=np.uint32) queue = deque([(self.cluster_tree_, np.arange(n_samples))]) diff --git a/unsupervised_bias_detection/cluster/_cluster_node.py b/unsupervised_bias_detection/cluster/_cluster_node.py index de8dc0b..bbc4eb2 100644 --- a/unsupervised_bias_detection/cluster/_cluster_node.py +++ b/unsupervised_bias_detection/cluster/_cluster_node.py @@ -1,12 +1,13 @@ -import itertools -from sklearn.base import ClusterMixin from typing import Self +from sklearn.base import ClusterMixin + + class ClusterNode: def __init__(self, label: int, score: float): """ Initialize a node in the cluster tree. - + Parameters ---------- label : int @@ -16,17 +17,17 @@ def __init__(self, label: int, score: float): self.score = score self.clustering_model = None self.children = [] - + @property def is_leaf(self): return len(self.children) == 0 - + def split(self, clustering_model: ClusterMixin, children: list[Self]): """ Split this node by setting its clustering model and adding children. - + This converts the node to an internal node and removes its label - + Parameters ---------- clustering_model : ClusterMixin @@ -37,11 +38,11 @@ def split(self, clustering_model: ClusterMixin, children: list[Self]): self.label = None self.clustering_model = clustering_model self.children = children - + def get_leaves(self) -> list[Self]: """ Get all leaf nodes in the subtree rooted at this node. - + Returns ------- list of ClusterNode @@ -49,8 +50,8 @@ def get_leaves(self) -> list[Self]: """ if not self.children: return [self] - + leaves = [] for child in self.children: leaves.extend(child.get_leaves()) - return leaves \ No newline at end of file + return leaves diff --git a/unsupervised_bias_detection/cluster/_kmeans.py b/unsupervised_bias_detection/cluster/_kmeans.py index 5906be0..5968634 100644 --- a/unsupervised_bias_detection/cluster/_kmeans.py +++ b/unsupervised_bias_detection/cluster/_kmeans.py @@ -1,7 +1,8 @@ -from ._bahc import BiasAwareHierarchicalClustering from sklearn.base import BaseEstimator, ClusterMixin from sklearn.cluster import KMeans +from ._bahc import BiasAwareHierarchicalClustering + class BiasAwareHierarchicalKMeans(BaseEstimator, ClusterMixin): """Bias-Aware Hierarchical k-Means Clustering. @@ -9,33 +10,37 @@ class BiasAwareHierarchicalKMeans(BaseEstimator, ClusterMixin): Parameters ---------- bahc_max_iter : int - Maximum number of iterations. + Maximum number of iterations to run the hierarchical splitting procedure. bahc_min_cluster_size : int - Minimum size of a cluster. - kmeans_params : dict - k-means parameters + The minimum size a cluster must have to be further split. + **kmeans_params : dict + Additional hyperparameters to pass to KMeans upon instantiation. Attributes ---------- n_clusters_ : int The number of clusters found by the algorithm. labels_ : ndarray of shape (n_samples,) - Cluster labels for each point. Lower labels correspond to higher discrimination scores. + Cluster labels for each point. + Lower labels correspond to higher discrimination scores. scores_ : ndarray of shape (n_clusters_,) Discrimination scores for each cluster. References ---------- - .. [1] J. Misztal-Radecka, B. Indurkhya, "Bias-Aware Hierarchical Clustering for detecting the discriminated - groups of users in recommendation systems", Information Processing & Management, vol. 58, no. 3, May. 2021. + .. [1] J. Misztal-Radecka, B. Indurkhya, "Bias-Aware Hierarchical Clustering + for detecting the discriminated groups of users in recommendation systems", + Information Processing & Management, vol. 58, no. 3, May. 2021. Examples -------- - >>> from unsupervised_bias_detection.clustering import BiasAwareHierarchicalKMeans + >>> from unsupervised_bias_detection.cluster import BiasAwareHierarchicalKMeans >>> import numpy as np >>> X = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]]) >>> y = np.array([0, 0, 0, 10, 10, 10]) - >>> bahc = BiasAwareHierarchicalKMeans(bahc_max_iter=1, bahc_min_cluster_size=1, random_state=12).fit(X, y) + >>> bahc = BiasAwareHierarchicalKMeans( + ... bahc_max_iter=1, bahc_min_cluster_size=1, random_state=12 + ... ).fit(X, y) >>> bahc.labels_ array([0, 0, 0, 1, 1, 1], dtype=uint32) >>> bahc.scores_ @@ -70,6 +75,6 @@ def fit(self, X, y): self.scores_ = self._bahc.scores_ self.cluster_tree_ = self._bahc.cluster_tree_ return self - + def predict(self, X): - return self._bahc.predict(X) \ No newline at end of file + return self._bahc.predict(X) diff --git a/unsupervised_bias_detection/cluster/_kmodes.py b/unsupervised_bias_detection/cluster/_kmodes.py index 82cbf44..2140ffb 100644 --- a/unsupervised_bias_detection/cluster/_kmodes.py +++ b/unsupervised_bias_detection/cluster/_kmodes.py @@ -1,7 +1,8 @@ -from ._bahc import BiasAwareHierarchicalClustering from kmodes.kmodes import KModes from sklearn.base import BaseEstimator, ClusterMixin +from ._bahc import BiasAwareHierarchicalClustering + class BiasAwareHierarchicalKModes(BaseEstimator, ClusterMixin): """Bias-Aware Hierarchical k-Modes Clustering. @@ -9,33 +10,37 @@ class BiasAwareHierarchicalKModes(BaseEstimator, ClusterMixin): Parameters ---------- bahc_max_iter : int - Maximum number of iterations. + Maximum number of iterations to run the hierarchical splitting procedure. bahc_min_cluster_size : int - Minimum size of a cluster. - kmodes_params : dict - k-modes parameters + The minimum size a cluster must have to be further split. + **kmodes_params : dict + Additional hyperparameters to pass to KModes upon instantiation. Attributes ---------- n_clusters_ : int The number of clusters found by the algorithm. labels_ : ndarray of shape (n_samples,) - Cluster labels for each point. Lower labels correspond to higher discrimination scores. + Cluster labels for each point. + Lower labels correspond to higher discrimination scores. scores_ : ndarray of shape (n_clusters_,) Discrimination scores for each cluster. References ---------- - .. [1] J. Misztal-Radecka, B. Indurkhya, "Bias-Aware Hierarchical Clustering for detecting the discriminated - groups of users in recommendation systems", Information Processing & Management, vol. 58, no. 3, May. 2021. + .. [1] J. Misztal-Radecka, B. Indurkhya, "Bias-Aware Hierarchical Clustering + for detecting the discriminated groups of users in recommendation systems", + Information Processing & Management, vol. 58, no. 3, May. 2021. Examples -------- - >>> from unsupervised_bias_detection.clustering import BiasAwareHierarchicalKModes + >>> from unsupervised_bias_detection.cluster import BiasAwareHierarchicalKModes >>> import numpy as np >>> X = np.array([[0, 1], [0, 2], [0, 0], [1, 4], [1, 5], [1, 3]]) >>> y = np.array([0, 0, 0, 10, 10, 10]) - >>> bahc = BiasAwareHierarchicalKModes(bahc_max_iter=1, bahc_min_cluster_size=1, random_state=12).fit(X, y) + >>> bahc = BiasAwareHierarchicalKModes( + ... bahc_max_iter=1, bahc_min_cluster_size=1, random_state=12 + ... ).fit(X, y) >>> bahc.labels_ array([0, 0, 0, 1, 1, 1], dtype=uint32) >>> bahc.scores_ @@ -48,17 +53,17 @@ def __init__(self, bahc_max_iter, bahc_min_cluster_size, **kmodes_params): self.bahc_max_iter = bahc_max_iter self.bahc_min_cluster_size = bahc_min_cluster_size - self._hbac = BiasAwareHierarchicalClustering( + self._bahc = BiasAwareHierarchicalClustering( KModes, bahc_max_iter, bahc_min_cluster_size, **kmodes_params ) def fit(self, X, y): - self._hbac.fit(X, y) - self.n_clusters_ = self._hbac.n_clusters_ - self.labels_ = self._hbac.labels_ - self.scores_ = self._hbac.scores_ - self.cluster_tree_ = self._hbac.cluster_tree_ + self._bahc.fit(X, y) + self.n_clusters_ = self._bahc.n_clusters_ + self.labels_ = self._bahc.labels_ + self.scores_ = self._bahc.scores_ + self.cluster_tree_ = self._bahc.cluster_tree_ return self def predict(self, X): - return self._hbac.predict(X) \ No newline at end of file + return self._bahc.predict(X) diff --git a/unsupervised_bias_detection/utils/__init__.py b/unsupervised_bias_detection/utils/__init__.py index 3806e4a..c38dd99 100644 --- a/unsupervised_bias_detection/utils/__init__.py +++ b/unsupervised_bias_detection/utils/__init__.py @@ -1,7 +1,9 @@ """The :mod:`unsupervised_bias_detection.utils` module implements utility functions.""" from ._get_column_dtypes import get_column_dtypes +from .dataset import load_default_dataset __all__ = [ "get_column_dtypes", -] \ No newline at end of file + "load_default_dataset", +] diff --git a/unsupervised_bias_detection/utils/_get_column_dtypes.py b/unsupervised_bias_detection/utils/_get_column_dtypes.py index 868b0b6..e5115ca 100644 --- a/unsupervised_bias_detection/utils/_get_column_dtypes.py +++ b/unsupervised_bias_detection/utils/_get_column_dtypes.py @@ -5,7 +5,7 @@ def get_column_dtypes(data) -> dict: """ Return a dictionary mapping column names to abstract data types that are compatible with the processor. - + The mapping is as follows: - float64, float32, int64, int32 -> "numerical" - bool -> "boolean" @@ -13,21 +13,25 @@ def get_column_dtypes(data) -> dict: - timedelta64[...] -> "timedelta" - All others (e.g., object) -> "categorical" """ + def map_dtype(dtype: str) -> str: - if dtype in ['float64', 'float32', 'int64', 'int32']: + if dtype in ["float64", "float32", "int64", "int32"]: return "numerical" - elif dtype == 'bool': + elif dtype == "bool": return "boolean" - elif 'datetime' in dtype: + elif "datetime" in dtype: return "datetime" - elif 'timedelta' in dtype: + elif "timedelta" in dtype: return "timedelta" else: return "categorical" - + if isinstance(data, pd.DataFrame): return {col: map_dtype(str(dtype)) for col, dtype in data.dtypes.items()} elif isinstance(data, np.ndarray) and data.dtype.names is not None: - return {name: map_dtype(str(data.dtype.fields[name][0])) for name in data.dtype.names} + return { + name: map_dtype(str(data.dtype.fields[name][0])) + for name in data.dtype.names + } else: - raise TypeError("Data must be a pandas DataFrame or a structured numpy array.") \ No newline at end of file + raise TypeError("Data must be a pandas DataFrame or a structured numpy array.") diff --git a/unsupervised_bias_detection/utils/dataset.py b/unsupervised_bias_detection/utils/dataset.py index fe4a35e..eae3c9f 100644 --- a/unsupervised_bias_detection/utils/dataset.py +++ b/unsupervised_bias_detection/utils/dataset.py @@ -29,8 +29,9 @@ def load_default_dataset(): -------- >>> from unsupervised_bias_detection.utils import load_default_dataset >>> x, y = load_default_dataset() + Note: it is up to the user to train a model with the provided data now before running the bias detection tool whether it is via the Algorithm Audit website for a demo or via the unsupervised_bias_detection package. >>> x - pandas.dataframe( race gender age ... had_outpatient_days readmitted readmit_binary + race gender age ... had_outpatient_days readmitted readmit_binary 0 Caucasian Female '30 years or younger' ... False NO 0 1 Caucasian Female '30 years or younger' ... False >30 1 2 AfricanAmerican Female '30 years or younger' ... True NO 0 @@ -41,21 +42,21 @@ def load_default_dataset(): 101762 AfricanAmerican Female 'Over 60 years' ... False NO 0 101763 Caucasian Male 'Over 60 years' ... True NO 0 101764 Caucasian Female 'Over 60 years' ... False NO 0 - 101765 Caucasian Male 'Over 60 years' ... False NO 0) - + 101765 Caucasian Male 'Over 60 years' ... False NO 0 + [101766 rows x 24 columns] >>> y - pandas.series( 0 0 - 1 0 - 2 0 - 3 0 - 4 0 - .. - 101761 0 - 101762 0 - 101763 0 - 101764 0 - 101765 0) + 0 0 + 1 0 + 2 0 + 3 0 + 4 0 + .. + 101761 0 + 101762 0 + 101763 0 + 101764 0 + 101765 0 Name: readmit_30_days, Length: 101766, dtype: int64 """ print( diff --git a/unsupervised_bias_detection/utils/validation.py b/unsupervised_bias_detection/utils/validation.py index 0c8ccde..befa5ee 100644 --- a/unsupervised_bias_detection/utils/validation.py +++ b/unsupervised_bias_detection/utils/validation.py @@ -1,7 +1,6 @@ """Provides functions for testing dataset properties.""" import pandas as pd - # TODO: add functionality to complete checks if dealing with a numpy array instead of pandas @@ -99,14 +98,17 @@ def run_checks(data): Example -------- >>> from unsupervised_bias_detection.utils.validation import run_checks - >>>data_dict = {'x': [[1, 2, 3], [3, 2, 1],[4, 5, 6]], 'preds': [0, 1, 1], 'true_labels': [0, 0, 1]} - >>>data_df = pd.DataFrame(data=data_dict) - >>>data_df + >>> import pandas as pd + >>> data_dict = {'x': [[1, 2, 3], [3, 2, 1],[4, 5, 6]], 'preds': [0, 1, 1], 'true_labels': [0, 0, 1]} + >>> data_df = pd.DataFrame(data=data_dict) + >>> data_df x preds true_labels 0 [1, 2, 3] 0 0 1 [3, 2, 1] 1 0 2 [4, 5, 6] 1 1 >>> run_checks(data_df) + Beginning testing... + No errors, finished testing. """ print("Beginning testing...") features, predictions, true_labels = _data_preprocessing(data) diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..2eab720 --- /dev/null +++ b/uv.lock @@ -0,0 +1,454 @@ +version = 1 +revision = 3 +requires-python = ">=3.11" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version < '3.14' and sys_platform == 'emscripten'", + "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", +] + +[[package]] +name = "colorama" +version = "0.4.6" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697, upload-time = "2022-10-25T02:36:22.414Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" }, +] + +[[package]] +name = "fairlearn" +version = "0.13.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "narwhals" }, + { name = "numpy" }, + { name = "pandas" }, + { name = "scikit-learn" }, + { name = "scipy" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/8d/66/822cdbfc85908cebc0314a8ebb238dee548f7d8980803ff80125ce1b5325/fairlearn-0.13.0.tar.gz", hash = "sha256:58ef3d0f100b10e21427f34687cc1ff1809678d089e22b0ecb4b6aef300c6719", size = 184051, upload-time = "2025-10-19T19:47:52.452Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/94/48/6d29ca1982e27442feecf2f15a1bbb477dc1d35187c3394235f2ecbbbd10/fairlearn-0.13.0-py3-none-any.whl", hash = "sha256:f322eecac24c4799c9ea6cf6f203bf1a76db5d4c13182f887905974b344adc91", size = 251625, upload-time = "2025-10-19T19:47:51.308Z" }, +] + +[[package]] +name = "iniconfig" +version = "2.3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503, upload-time = "2025-10-18T21:55:43.219Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484, upload-time = "2025-10-18T21:55:41.639Z" }, +] + +[[package]] +name = "joblib" +version = "1.5.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/41/f2/d34e8b3a08a9cc79a50b2208a93dce981fe615b64d5a4d4abee421d898df/joblib-1.5.3.tar.gz", hash = "sha256:8561a3269e6801106863fd0d6d84bb737be9e7631e33aaed3fb9ce5953688da3", size = 331603, upload-time = "2025-12-15T08:41:46.427Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7b/91/984aca2ec129e2757d1e4e3c81c3fcda9d0f85b74670a094cc443d9ee949/joblib-1.5.3-py3-none-any.whl", hash = "sha256:5fc3c5039fc5ca8c0276333a188bbd59d6b7ab37fe6632daa76bc7f9ec18e713", size = 309071, upload-time = "2025-12-15T08:41:44.973Z" }, +] + +[[package]] +name = "kmodes" +version = "0.12.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "joblib" }, + { name = "numpy" }, + { name = "scikit-learn" }, + { name = "scipy" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/5c/16/e2b5222bb5efabc56259215bbc9e94df9ee08a43b5a9ed33aae28ce2dade/kmodes-0.12.2.tar.gz", hash = "sha256:d840ac9f4616a668ebacba24a12ec1def87da24a9fd0a0dc2f7499a9b9a6f45b", size = 18883, upload-time = "2022-09-06T19:36:37.14Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1a/a8/0d3bf6f3340cbcb8cf4ad02c306d157af8f09ce86aadf5346e00605870dd/kmodes-0.12.2-py2.py3-none-any.whl", hash = "sha256:b764f7166dd5fe63826135ed74df796693dc7c25fc2cb8a106e14f3bfb371004", size = 20408, upload-time = "2022-09-06T19:36:34.956Z" }, +] + +[[package]] +name = "narwhals" +version = "2.17.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/75/59/81d0f4cad21484083466f278e6b392addd9f4205b48d45b5c8771670ebf8/narwhals-2.17.0.tar.gz", hash = "sha256:ebd5bc95bcfa2f8e89a8ac09e2765a63055162837208e67b42d6eeb6651d5e67", size = 620306, upload-time = "2026-02-23T09:44:34.142Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/4b/27/20770bd6bf8fbe1e16f848ba21da9df061f38d2e6483952c29d2bb5d1d8b/narwhals-2.17.0-py3-none-any.whl", hash = "sha256:2ac5307b7c2b275a7d66eeda906b8605e3d7a760951e188dcfff86e8ebe083dd", size = 444897, upload-time = "2026-02-23T09:44:32.006Z" }, +] + +[[package]] +name = "numpy" +version = "2.4.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/10/8b/c265f4823726ab832de836cdd184d0986dcf94480f81e8739692a7ac7af2/numpy-2.4.3.tar.gz", hash = "sha256:483a201202b73495f00dbc83796c6ae63137a9bdade074f7648b3e32613412dd", size = 20727743, upload-time = "2026-03-09T07:58:53.426Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f9/51/5093a2df15c4dc19da3f79d1021e891f5dcf1d9d1db6ba38891d5590f3fe/numpy-2.4.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:33b3bf58ee84b172c067f56aeadc7ee9ab6de69c5e800ab5b10295d54c581adb", size = 16957183, upload-time = "2026-03-09T07:55:57.774Z" }, + { url = "https://files.pythonhosted.org/packages/b5/7c/c061f3de0630941073d2598dc271ac2f6cbcf5c83c74a5870fea07488333/numpy-2.4.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8ba7b51e71c05aa1f9bc3641463cd82308eab40ce0d5c7e1fd4038cbf9938147", size = 14968734, upload-time = "2026-03-09T07:56:00.494Z" }, + { url = "https://files.pythonhosted.org/packages/ef/27/d26c85cbcd86b26e4f125b0668e7a7c0542d19dd7d23ee12e87b550e95b5/numpy-2.4.3-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:a1988292870c7cb9d0ebb4cc96b4d447513a9644801de54606dc7aabf2b7d920", size = 5475288, upload-time = "2026-03-09T07:56:02.857Z" }, + { url = "https://files.pythonhosted.org/packages/2b/09/3c4abbc1dcd8010bf1a611d174c7aa689fc505585ec806111b4406f6f1b1/numpy-2.4.3-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:23b46bb6d8ecb68b58c09944483c135ae5f0e9b8d8858ece5e4ead783771d2a9", size = 6805253, upload-time = "2026-03-09T07:56:04.53Z" }, + { url = "https://files.pythonhosted.org/packages/21/bc/e7aa3f6817e40c3f517d407742337cbb8e6fc4b83ce0b55ab780c829243b/numpy-2.4.3-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a016db5c5dba78fa8fe9f5d80d6708f9c42ab087a739803c0ac83a43d686a470", size = 15969479, upload-time = "2026-03-09T07:56:06.638Z" }, + { url = "https://files.pythonhosted.org/packages/78/51/9f5d7a41f0b51649ddf2f2320595e15e122a40610b233d51928dd6c92353/numpy-2.4.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:715de7f82e192e8cae5a507a347d97ad17598f8e026152ca97233e3666daaa71", size = 16901035, upload-time = "2026-03-09T07:56:09.405Z" }, + { url = "https://files.pythonhosted.org/packages/64/6e/b221dd847d7181bc5ee4857bfb026182ef69499f9305eb1371cbb1aea626/numpy-2.4.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:2ddb7919366ee468342b91dea2352824c25b55814a987847b6c52003a7c97f15", size = 17325657, upload-time = "2026-03-09T07:56:12.067Z" }, + { url = "https://files.pythonhosted.org/packages/eb/b8/8f3fd2da596e1063964b758b5e3c970aed1949a05200d7e3d46a9d46d643/numpy-2.4.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:a315e5234d88067f2d97e1f2ef670a7569df445d55400f1e33d117418d008d52", size = 18635512, upload-time = "2026-03-09T07:56:14.629Z" }, + { url = "https://files.pythonhosted.org/packages/5c/24/2993b775c37e39d2f8ab4125b44337ab0b2ba106c100980b7c274a22bee7/numpy-2.4.3-cp311-cp311-win32.whl", hash = "sha256:2b3f8d2c4589b1a2028d2a770b0fc4d1f332fb5e01521f4de3199a896d158ddd", size = 6238100, upload-time = "2026-03-09T07:56:17.243Z" }, + { url = "https://files.pythonhosted.org/packages/76/1d/edccf27adedb754db7c4511d5eac8b83f004ae948fe2d3509e8b78097d4c/numpy-2.4.3-cp311-cp311-win_amd64.whl", hash = "sha256:77e76d932c49a75617c6d13464e41203cd410956614d0a0e999b25e9e8d27eec", size = 12609816, upload-time = "2026-03-09T07:56:19.089Z" }, + { url = "https://files.pythonhosted.org/packages/92/82/190b99153480076c8dce85f4cfe7d53ea84444145ffa54cb58dcd460d66b/numpy-2.4.3-cp311-cp311-win_arm64.whl", hash = "sha256:eb610595dd91560905c132c709412b512135a60f1851ccbd2c959e136431ff67", size = 10485757, upload-time = "2026-03-09T07:56:21.753Z" }, + { url = "https://files.pythonhosted.org/packages/a9/ed/6388632536f9788cea23a3a1b629f25b43eaacd7d7377e5d6bc7b9deb69b/numpy-2.4.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:61b0cbabbb6126c8df63b9a3a0c4b1f44ebca5e12ff6997b80fcf267fb3150ef", size = 16669628, upload-time = "2026-03-09T07:56:24.252Z" }, + { url = "https://files.pythonhosted.org/packages/74/1b/ee2abfc68e1ce728b2958b6ba831d65c62e1b13ce3017c13943f8f9b5b2e/numpy-2.4.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:7395e69ff32526710748f92cd8c9849b361830968ea3e24a676f272653e8983e", size = 14696872, upload-time = "2026-03-09T07:56:26.991Z" }, + { url = "https://files.pythonhosted.org/packages/ba/d1/780400e915ff5638166f11ca9dc2c5815189f3d7cf6f8759a1685e586413/numpy-2.4.3-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:abdce0f71dcb4a00e4e77f3faf05e4616ceccfe72ccaa07f47ee79cda3b7b0f4", size = 5203489, upload-time = "2026-03-09T07:56:29.414Z" }, + { url = "https://files.pythonhosted.org/packages/0b/bb/baffa907e9da4cc34a6e556d6d90e032f6d7a75ea47968ea92b4858826c4/numpy-2.4.3-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:48da3a4ee1336454b07497ff7ec83903efa5505792c4e6d9bf83d99dc07a1e18", size = 6550814, upload-time = "2026-03-09T07:56:32.225Z" }, + { url = "https://files.pythonhosted.org/packages/7b/12/8c9f0c6c95f76aeb20fc4a699c33e9f827fa0d0f857747c73bb7b17af945/numpy-2.4.3-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:32e3bef222ad6b052280311d1d60db8e259e4947052c3ae7dd6817451fc8a4c5", size = 15666601, upload-time = "2026-03-09T07:56:34.461Z" }, + { url = "https://files.pythonhosted.org/packages/bd/79/cc665495e4d57d0aa6fbcc0aa57aa82671dfc78fbf95fe733ed86d98f52a/numpy-2.4.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e7dd01a46700b1967487141a66ac1a3cf0dd8ebf1f08db37d46389401512ca97", size = 16621358, upload-time = "2026-03-09T07:56:36.852Z" }, + { url = "https://files.pythonhosted.org/packages/a8/40/b4ecb7224af1065c3539f5ecfff879d090de09608ad1008f02c05c770cb3/numpy-2.4.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:76f0f283506c28b12bba319c0fab98217e9f9b54e6160e9c79e9f7348ba32e9c", size = 17016135, upload-time = "2026-03-09T07:56:39.337Z" }, + { url = "https://files.pythonhosted.org/packages/f7/b1/6a88e888052eed951afed7a142dcdf3b149a030ca59b4c71eef085858e43/numpy-2.4.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:737f630a337364665aba3b5a77e56a68cc42d350edd010c345d65a3efa3addcc", size = 18345816, upload-time = "2026-03-09T07:56:42.31Z" }, + { url = "https://files.pythonhosted.org/packages/f3/8f/103a60c5f8c3d7fc678c19cd7b2476110da689ccb80bc18050efbaeae183/numpy-2.4.3-cp312-cp312-win32.whl", hash = "sha256:26952e18d82a1dbbc2f008d402021baa8d6fc8e84347a2072a25e08b46d698b9", size = 5960132, upload-time = "2026-03-09T07:56:44.851Z" }, + { url = "https://files.pythonhosted.org/packages/d7/7c/f5ee1bf6ed888494978046a809df2882aad35d414b622893322df7286879/numpy-2.4.3-cp312-cp312-win_amd64.whl", hash = "sha256:65f3c2455188f09678355f5cae1f959a06b778bc66d535da07bf2ef20cd319d5", size = 12316144, upload-time = "2026-03-09T07:56:47.057Z" }, + { url = "https://files.pythonhosted.org/packages/71/46/8d1cb3f7a00f2fb6394140e7e6623696e54c6318a9d9691bb4904672cf42/numpy-2.4.3-cp312-cp312-win_arm64.whl", hash = "sha256:2abad5c7fef172b3377502bde47892439bae394a71bc329f31df0fd829b41a9e", size = 10220364, upload-time = "2026-03-09T07:56:49.849Z" }, + { url = "https://files.pythonhosted.org/packages/b6/d0/1fe47a98ce0df229238b77611340aff92d52691bcbc10583303181abf7fc/numpy-2.4.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:b346845443716c8e542d54112966383b448f4a3ba5c66409771b8c0889485dd3", size = 16665297, upload-time = "2026-03-09T07:56:52.296Z" }, + { url = "https://files.pythonhosted.org/packages/27/d9/4e7c3f0e68dfa91f21c6fb6cf839bc829ec920688b1ce7ec722b1a6202fb/numpy-2.4.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2629289168f4897a3c4e23dc98d6f1731f0fc0fe52fb9db19f974041e4cc12b9", size = 14691853, upload-time = "2026-03-09T07:56:54.992Z" }, + { url = "https://files.pythonhosted.org/packages/3a/66/bd096b13a87549683812b53ab211e6d413497f84e794fb3c39191948da97/numpy-2.4.3-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:bb2e3cf95854233799013779216c57e153c1ee67a0bf92138acca0e429aefaee", size = 5198435, upload-time = "2026-03-09T07:56:57.184Z" }, + { url = "https://files.pythonhosted.org/packages/a2/2f/687722910b5a5601de2135c891108f51dfc873d8e43c8ed9f4ebb440b4a2/numpy-2.4.3-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:7f3408ff897f8ab07a07fbe2823d7aee6ff644c097cc1f90382511fe982f647f", size = 6546347, upload-time = "2026-03-09T07:56:59.531Z" }, + { url = "https://files.pythonhosted.org/packages/bf/ec/7971c4e98d86c564750393fab8d7d83d0a9432a9d78bb8a163a6dc59967a/numpy-2.4.3-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:decb0eb8a53c3b009b0962378065589685d66b23467ef5dac16cbe818afde27f", size = 15664626, upload-time = "2026-03-09T07:57:01.385Z" }, + { url = "https://files.pythonhosted.org/packages/7e/eb/7daecbea84ec935b7fc732e18f532073064a3816f0932a40a17f3349185f/numpy-2.4.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d5f51900414fc9204a0e0da158ba2ac52b75656e7dce7e77fb9f84bfa343b4cc", size = 16608916, upload-time = "2026-03-09T07:57:04.008Z" }, + { url = "https://files.pythonhosted.org/packages/df/58/2a2b4a817ffd7472dca4421d9f0776898b364154e30c95f42195041dc03b/numpy-2.4.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:6bd06731541f89cdc01b261ba2c9e037f1543df7472517836b78dfb15bd6e476", size = 17015824, upload-time = "2026-03-09T07:57:06.347Z" }, + { url = "https://files.pythonhosted.org/packages/4a/ca/627a828d44e78a418c55f82dd4caea8ea4a8ef24e5144d9e71016e52fb40/numpy-2.4.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:22654fe6be0e5206f553a9250762c653d3698e46686eee53b399ab90da59bd92", size = 18334581, upload-time = "2026-03-09T07:57:09.114Z" }, + { url = "https://files.pythonhosted.org/packages/cd/c0/76f93962fc79955fcba30a429b62304332345f22d4daec1cb33653425643/numpy-2.4.3-cp313-cp313-win32.whl", hash = "sha256:d71e379452a2f670ccb689ec801b1218cd3983e253105d6e83780967e899d687", size = 5958618, upload-time = "2026-03-09T07:57:11.432Z" }, + { url = "https://files.pythonhosted.org/packages/b1/3c/88af0040119209b9b5cb59485fa48b76f372c73068dbf9254784b975ac53/numpy-2.4.3-cp313-cp313-win_amd64.whl", hash = "sha256:0a60e17a14d640f49146cb38e3f105f571318db7826d9b6fef7e4dce758faecd", size = 12312824, upload-time = "2026-03-09T07:57:13.586Z" }, + { url = "https://files.pythonhosted.org/packages/58/ce/3d07743aced3d173f877c3ef6a454c2174ba42b584ab0b7e6d99374f51ed/numpy-2.4.3-cp313-cp313-win_arm64.whl", hash = "sha256:c9619741e9da2059cd9c3f206110b97583c7152c1dc9f8aafd4beb450ac1c89d", size = 10221218, upload-time = "2026-03-09T07:57:16.183Z" }, + { url = "https://files.pythonhosted.org/packages/62/09/d96b02a91d09e9d97862f4fc8bfebf5400f567d8eb1fe4b0cc4795679c15/numpy-2.4.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:7aa4e54f6469300ebca1d9eb80acd5253cdfa36f2c03d79a35883687da430875", size = 14819570, upload-time = "2026-03-09T07:57:18.564Z" }, + { url = "https://files.pythonhosted.org/packages/b5/ca/0b1aba3905fdfa3373d523b2b15b19029f4f3031c87f4066bd9d20ef6c6b/numpy-2.4.3-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:d1b90d840b25874cf5cd20c219af10bac3667db3876d9a495609273ebe679070", size = 5326113, upload-time = "2026-03-09T07:57:21.052Z" }, + { url = "https://files.pythonhosted.org/packages/c0/63/406e0fd32fcaeb94180fd6a4c41e55736d676c54346b7efbce548b94a914/numpy-2.4.3-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:a749547700de0a20a6718293396ec237bb38218049cfce788e08fcb716e8cf73", size = 6646370, upload-time = "2026-03-09T07:57:22.804Z" }, + { url = "https://files.pythonhosted.org/packages/b6/d0/10f7dc157d4b37af92720a196be6f54f889e90dcd30dce9dc657ed92c257/numpy-2.4.3-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:94f3c4a151a2e529adf49c1d54f0f57ff8f9b233ee4d44af623a81553ab86368", size = 15723499, upload-time = "2026-03-09T07:57:24.693Z" }, + { url = "https://files.pythonhosted.org/packages/66/f1/d1c2bf1161396629701bc284d958dc1efa3a5a542aab83cf11ee6eb4cba5/numpy-2.4.3-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:22c31dc07025123aedf7f2db9e91783df13f1776dc52c6b22c620870dc0fab22", size = 16657164, upload-time = "2026-03-09T07:57:27.676Z" }, + { url = "https://files.pythonhosted.org/packages/1a/be/cca19230b740af199ac47331a21c71e7a3d0ba59661350483c1600d28c37/numpy-2.4.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:148d59127ac95979d6f07e4d460f934ebdd6eed641db9c0db6c73026f2b2101a", size = 17081544, upload-time = "2026-03-09T07:57:30.664Z" }, + { url = "https://files.pythonhosted.org/packages/b9/c5/9602b0cbb703a0936fb40f8a95407e8171935b15846de2f0776e08af04c7/numpy-2.4.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:a97cbf7e905c435865c2d939af3d93f99d18eaaa3cabe4256f4304fb51604349", size = 18380290, upload-time = "2026-03-09T07:57:33.763Z" }, + { url = "https://files.pythonhosted.org/packages/ed/81/9f24708953cd30be9ee36ec4778f4b112b45165812f2ada4cc5ea1c1f254/numpy-2.4.3-cp313-cp313t-win32.whl", hash = "sha256:be3b8487d725a77acccc9924f65fd8bce9af7fac8c9820df1049424a2115af6c", size = 6082814, upload-time = "2026-03-09T07:57:36.491Z" }, + { url = "https://files.pythonhosted.org/packages/e2/9e/52f6eaa13e1a799f0ab79066c17f7016a4a8ae0c1aefa58c82b4dab690b4/numpy-2.4.3-cp313-cp313t-win_amd64.whl", hash = "sha256:1ec84fd7c8e652b0f4aaaf2e6e9cc8eaa9b1b80a537e06b2e3a2fb176eedcb26", size = 12452673, upload-time = "2026-03-09T07:57:38.281Z" }, + { url = "https://files.pythonhosted.org/packages/c4/04/b8cece6ead0b30c9fbd99bb835ad7ea0112ac5f39f069788c5558e3b1ab2/numpy-2.4.3-cp313-cp313t-win_arm64.whl", hash = "sha256:120df8c0a81ebbf5b9020c91439fccd85f5e018a927a39f624845be194a2be02", size = 10290907, upload-time = "2026-03-09T07:57:40.747Z" }, + { url = "https://files.pythonhosted.org/packages/70/ae/3936f79adebf8caf81bd7a599b90a561334a658be4dcc7b6329ebf4ee8de/numpy-2.4.3-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:5884ce5c7acfae1e4e1b6fde43797d10aa506074d25b531b4f54bde33c0c31d4", size = 16664563, upload-time = "2026-03-09T07:57:43.817Z" }, + { url = "https://files.pythonhosted.org/packages/9b/62/760f2b55866b496bb1fa7da2a6db076bef908110e568b02fcfc1422e2a3a/numpy-2.4.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:297837823f5bc572c5f9379b0c9f3a3365f08492cbdc33bcc3af174372ebb168", size = 14702161, upload-time = "2026-03-09T07:57:46.169Z" }, + { url = "https://files.pythonhosted.org/packages/32/af/a7a39464e2c0a21526fb4fb76e346fb172ebc92f6d1c7a07c2c139cc17b1/numpy-2.4.3-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:a111698b4a3f8dcbe54c64a7708f049355abd603e619013c346553c1fd4ca90b", size = 5208738, upload-time = "2026-03-09T07:57:48.506Z" }, + { url = "https://files.pythonhosted.org/packages/29/8c/2a0cf86a59558fa078d83805589c2de490f29ed4fb336c14313a161d358a/numpy-2.4.3-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:4bd4741a6a676770e0e97fe9ab2e51de01183df3dcbcec591d26d331a40de950", size = 6543618, upload-time = "2026-03-09T07:57:50.591Z" }, + { url = "https://files.pythonhosted.org/packages/aa/b8/612ce010c0728b1c363fa4ea3aa4c22fe1c5da1de008486f8c2f5cb92fae/numpy-2.4.3-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:54f29b877279d51e210e0c80709ee14ccbbad647810e8f3d375561c45ef613dd", size = 15680676, upload-time = "2026-03-09T07:57:52.34Z" }, + { url = "https://files.pythonhosted.org/packages/a9/7e/4f120ecc54ba26ddf3dc348eeb9eb063f421de65c05fc961941798feea18/numpy-2.4.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:679f2a834bae9020f81534671c56fd0cc76dd7e5182f57131478e23d0dc59e24", size = 16613492, upload-time = "2026-03-09T07:57:54.91Z" }, + { url = "https://files.pythonhosted.org/packages/2c/86/1b6020db73be330c4b45d5c6ee4295d59cfeef0e3ea323959d053e5a6909/numpy-2.4.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:d84f0f881cb2225c2dfd7f78a10a5645d487a496c6668d6cc39f0f114164f3d0", size = 17031789, upload-time = "2026-03-09T07:57:57.641Z" }, + { url = "https://files.pythonhosted.org/packages/07/3a/3b90463bf41ebc21d1b7e06079f03070334374208c0f9a1f05e4ae8455e7/numpy-2.4.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:d213c7e6e8d211888cc359bab7199670a00f5b82c0978b9d1c75baf1eddbeac0", size = 18339941, upload-time = "2026-03-09T07:58:00.577Z" }, + { url = "https://files.pythonhosted.org/packages/a8/74/6d736c4cd962259fd8bae9be27363eb4883a2f9069763747347544c2a487/numpy-2.4.3-cp314-cp314-win32.whl", hash = "sha256:52077feedeff7c76ed7c9f1a0428558e50825347b7545bbb8523da2cd55c547a", size = 6007503, upload-time = "2026-03-09T07:58:03.331Z" }, + { url = "https://files.pythonhosted.org/packages/48/39/c56ef87af669364356bb011922ef0734fc49dad51964568634c72a009488/numpy-2.4.3-cp314-cp314-win_amd64.whl", hash = "sha256:0448e7f9caefb34b4b7dd2b77f21e8906e5d6f0365ad525f9f4f530b13df2afc", size = 12444915, upload-time = "2026-03-09T07:58:06.353Z" }, + { url = "https://files.pythonhosted.org/packages/9d/1f/ab8528e38d295fd349310807496fabb7cf9fe2e1f70b97bc20a483ea9d4a/numpy-2.4.3-cp314-cp314-win_arm64.whl", hash = "sha256:b44fd60341c4d9783039598efadd03617fa28d041fc37d22b62d08f2027fa0e7", size = 10494875, upload-time = "2026-03-09T07:58:08.734Z" }, + { url = "https://files.pythonhosted.org/packages/e6/ef/b7c35e4d5ef141b836658ab21a66d1a573e15b335b1d111d31f26c8ef80f/numpy-2.4.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:0a195f4216be9305a73c0e91c9b026a35f2161237cf1c6de9b681637772ea657", size = 14822225, upload-time = "2026-03-09T07:58:11.034Z" }, + { url = "https://files.pythonhosted.org/packages/cd/8d/7730fa9278cf6648639946cc816e7cc89f0d891602584697923375f801ed/numpy-2.4.3-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:cd32fbacb9fd1bf041bf8e89e4576b6f00b895f06d00914820ae06a616bdfef7", size = 5328769, upload-time = "2026-03-09T07:58:13.67Z" }, + { url = "https://files.pythonhosted.org/packages/47/01/d2a137317c958b074d338807c1b6a383406cdf8b8e53b075d804cc3d211d/numpy-2.4.3-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:2e03c05abaee1f672e9d67bc858f300b5ccba1c21397211e8d77d98350972093", size = 6649461, upload-time = "2026-03-09T07:58:15.912Z" }, + { url = "https://files.pythonhosted.org/packages/5c/34/812ce12bc0f00272a4b0ec0d713cd237cb390666eb6206323d1cc9cedbb2/numpy-2.4.3-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7d1ce23cce91fcea443320a9d0ece9b9305d4368875bab09538f7a5b4131938a", size = 15725809, upload-time = "2026-03-09T07:58:17.787Z" }, + { url = "https://files.pythonhosted.org/packages/25/c0/2aed473a4823e905e765fee3dc2cbf504bd3e68ccb1150fbdabd5c39f527/numpy-2.4.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c59020932feb24ed49ffd03704fbab89f22aa9c0d4b180ff45542fe8918f5611", size = 16655242, upload-time = "2026-03-09T07:58:20.476Z" }, + { url = "https://files.pythonhosted.org/packages/f2/c8/7e052b2fc87aa0e86de23f20e2c42bd261c624748aa8efd2c78f7bb8d8c6/numpy-2.4.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:9684823a78a6cd6ad7511fc5e25b07947d1d5b5e2812c93fe99d7d4195130720", size = 17080660, upload-time = "2026-03-09T07:58:23.067Z" }, + { url = "https://files.pythonhosted.org/packages/f3/3d/0876746044db2adcb11549f214d104f2e1be00f07a67edbb4e2812094847/numpy-2.4.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:0200b25c687033316fb39f0ff4e3e690e8957a2c3c8d22499891ec58c37a3eb5", size = 18380384, upload-time = "2026-03-09T07:58:25.839Z" }, + { url = "https://files.pythonhosted.org/packages/07/12/8160bea39da3335737b10308df4f484235fd297f556745f13092aa039d3b/numpy-2.4.3-cp314-cp314t-win32.whl", hash = "sha256:5e10da9e93247e554bb1d22f8edc51847ddd7dde52d85ce31024c1b4312bfba0", size = 6154547, upload-time = "2026-03-09T07:58:28.289Z" }, + { url = "https://files.pythonhosted.org/packages/42/f3/76534f61f80d74cc9cdf2e570d3d4eeb92c2280a27c39b0aaf471eda7b48/numpy-2.4.3-cp314-cp314t-win_amd64.whl", hash = "sha256:45f003dbdffb997a03da2d1d0cb41fbd24a87507fb41605c0420a3db5bd4667b", size = 12633645, upload-time = "2026-03-09T07:58:30.384Z" }, + { url = "https://files.pythonhosted.org/packages/1f/b6/7c0d4334c15983cec7f92a69e8ce9b1e6f31857e5ee3a413ac424e6bd63d/numpy-2.4.3-cp314-cp314t-win_arm64.whl", hash = "sha256:4d382735cecd7bcf090172489a525cd7d4087bc331f7df9f60ddc9a296cf208e", size = 10565454, upload-time = "2026-03-09T07:58:33.031Z" }, + { url = "https://files.pythonhosted.org/packages/64/e4/4dab9fb43c83719c29241c535d9e07be73bea4bc0c6686c5816d8e1b6689/numpy-2.4.3-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:c6b124bfcafb9e8d3ed09130dbee44848c20b3e758b6bbf006e641778927c028", size = 16834892, upload-time = "2026-03-09T07:58:35.334Z" }, + { url = "https://files.pythonhosted.org/packages/c9/29/f8b6d4af90fed3dfda84ebc0df06c9833d38880c79ce954e5b661758aa31/numpy-2.4.3-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:76dbb9d4e43c16cf9aa711fcd8de1e2eeb27539dcefb60a1d5e9f12fae1d1ed8", size = 14893070, upload-time = "2026-03-09T07:58:37.7Z" }, + { url = "https://files.pythonhosted.org/packages/9a/04/a19b3c91dbec0a49269407f15d5753673a09832daed40c45e8150e6fa558/numpy-2.4.3-pp311-pypy311_pp73-macosx_14_0_arm64.whl", hash = "sha256:29363fbfa6f8ee855d7569c96ce524845e3d726d6c19b29eceec7dd555dab152", size = 5399609, upload-time = "2026-03-09T07:58:39.853Z" }, + { url = "https://files.pythonhosted.org/packages/79/34/4d73603f5420eab89ea8a67097b31364bf7c30f811d4dd84b1659c7476d9/numpy-2.4.3-pp311-pypy311_pp73-macosx_14_0_x86_64.whl", hash = "sha256:bc71942c789ef415a37f0d4eab90341425a00d538cd0642445d30b41023d3395", size = 6714355, upload-time = "2026-03-09T07:58:42.365Z" }, + { url = "https://files.pythonhosted.org/packages/58/ad/1100d7229bb248394939a12a8074d485b655e8ed44207d328fdd7fcebc7b/numpy-2.4.3-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7e58765ad74dcebd3ef0208a5078fba32dc8ec3578fe84a604432950cd043d79", size = 15800434, upload-time = "2026-03-09T07:58:44.837Z" }, + { url = "https://files.pythonhosted.org/packages/0c/fd/16d710c085d28ba4feaf29ac60c936c9d662e390344f94a6beaa2ac9899b/numpy-2.4.3-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8e236dbda4e1d319d681afcbb136c0c4a8e0f1a5c58ceec2adebb547357fe857", size = 16729409, upload-time = "2026-03-09T07:58:47.972Z" }, + { url = "https://files.pythonhosted.org/packages/57/a7/b35835e278c18b85206834b3aa3abe68e77a98769c59233d1f6300284781/numpy-2.4.3-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:4b42639cdde6d24e732ff823a3fa5b701d8acad89c4142bc1d0bd6dc85200ba5", size = 12504685, upload-time = "2026-03-09T07:58:50.525Z" }, +] + +[[package]] +name = "packaging" +version = "26.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/65/ee/299d360cdc32edc7d2cf530f3accf79c4fca01e96ffc950d8a52213bd8e4/packaging-26.0.tar.gz", hash = "sha256:00243ae351a257117b6a241061796684b084ed1c516a08c48a3f7e147a9d80b4", size = 143416, upload-time = "2026-01-21T20:50:39.064Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b7/b9/c538f279a4e237a006a2c98387d081e9eb060d203d8ed34467cc0f0b9b53/packaging-26.0-py3-none-any.whl", hash = "sha256:b36f1fef9334a5588b4166f8bcd26a14e521f2b55e6b9de3aaa80d3ff7a37529", size = 74366, upload-time = "2026-01-21T20:50:37.788Z" }, +] + +[[package]] +name = "pandas" +version = "3.0.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "numpy" }, + { name = "python-dateutil" }, + { name = "tzdata", marker = "sys_platform == 'emscripten' or sys_platform == 'win32'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/2e/0c/b28ed414f080ee0ad153f848586d61d1878f91689950f037f976ce15f6c8/pandas-3.0.1.tar.gz", hash = "sha256:4186a699674af418f655dbd420ed87f50d56b4cd6603784279d9eef6627823c8", size = 4641901, upload-time = "2026-02-17T22:20:16.434Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ff/07/c7087e003ceee9b9a82539b40414ec557aa795b584a1a346e89180853d79/pandas-3.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:de09668c1bf3b925c07e5762291602f0d789eca1b3a781f99c1c78f6cac0e7ea", size = 10323380, upload-time = "2026-02-17T22:18:16.133Z" }, + { url = "https://files.pythonhosted.org/packages/c1/27/90683c7122febeefe84a56f2cde86a9f05f68d53885cebcc473298dfc33e/pandas-3.0.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:24ba315ba3d6e5806063ac6eb717504e499ce30bd8c236d8693a5fd3f084c796", size = 9923455, upload-time = "2026-02-17T22:18:19.13Z" }, + { url = "https://files.pythonhosted.org/packages/0e/f1/ed17d927f9950643bc7631aa4c99ff0cc83a37864470bc419345b656a41f/pandas-3.0.1-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:406ce835c55bac912f2a0dcfaf27c06d73c6b04a5dde45f1fd3169ce31337389", size = 10753464, upload-time = "2026-02-17T22:18:21.134Z" }, + { url = "https://files.pythonhosted.org/packages/2e/7c/870c7e7daec2a6c7ff2ac9e33b23317230d4e4e954b35112759ea4a924a7/pandas-3.0.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:830994d7e1f31dd7e790045235605ab61cff6c94defc774547e8b7fdfbff3dc7", size = 11255234, upload-time = "2026-02-17T22:18:24.175Z" }, + { url = "https://files.pythonhosted.org/packages/5c/39/3653fe59af68606282b989c23d1a543ceba6e8099cbcc5f1d506a7bae2aa/pandas-3.0.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:a64ce8b0f2de1d2efd2ae40b0abe7f8ae6b29fbfb3812098ed5a6f8e235ad9bf", size = 11767299, upload-time = "2026-02-17T22:18:26.824Z" }, + { url = "https://files.pythonhosted.org/packages/9b/31/1daf3c0c94a849c7a8dab8a69697b36d313b229918002ba3e409265c7888/pandas-3.0.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:9832c2c69da24b602c32e0c7b1b508a03949c18ba08d4d9f1c1033426685b447", size = 12333292, upload-time = "2026-02-17T22:18:28.996Z" }, + { url = "https://files.pythonhosted.org/packages/1f/67/af63f83cd6ca603a00fe8530c10a60f0879265b8be00b5930e8e78c5b30b/pandas-3.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:84f0904a69e7365f79a0c77d3cdfccbfb05bf87847e3a51a41e1426b0edb9c79", size = 9892176, upload-time = "2026-02-17T22:18:31.79Z" }, + { url = "https://files.pythonhosted.org/packages/79/ab/9c776b14ac4b7b4140788eca18468ea39894bc7340a408f1d1e379856a6b/pandas-3.0.1-cp311-cp311-win_arm64.whl", hash = "sha256:4a68773d5a778afb31d12e34f7dd4612ab90de8c6fb1d8ffe5d4a03b955082a1", size = 9151328, upload-time = "2026-02-17T22:18:35.721Z" }, + { url = "https://files.pythonhosted.org/packages/37/51/b467209c08dae2c624873d7491ea47d2b47336e5403309d433ea79c38571/pandas-3.0.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:476f84f8c20c9f5bc47252b66b4bb25e1a9fc2fa98cead96744d8116cb85771d", size = 10344357, upload-time = "2026-02-17T22:18:38.262Z" }, + { url = "https://files.pythonhosted.org/packages/7c/f1/e2567ffc8951ab371db2e40b2fe068e36b81d8cf3260f06ae508700e5504/pandas-3.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:0ab749dfba921edf641d4036c4c21c0b3ea70fea478165cb98a998fb2a261955", size = 9884543, upload-time = "2026-02-17T22:18:41.476Z" }, + { url = "https://files.pythonhosted.org/packages/d7/39/327802e0b6d693182403c144edacbc27eb82907b57062f23ef5a4c4a5ea7/pandas-3.0.1-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b8e36891080b87823aff3640c78649b91b8ff6eea3c0d70aeabd72ea43ab069b", size = 10396030, upload-time = "2026-02-17T22:18:43.822Z" }, + { url = "https://files.pythonhosted.org/packages/3d/fe/89d77e424365280b79d99b3e1e7d606f5165af2f2ecfaf0c6d24c799d607/pandas-3.0.1-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:532527a701281b9dd371e2f582ed9094f4c12dd9ffb82c0c54ee28d8ac9520c4", size = 10876435, upload-time = "2026-02-17T22:18:45.954Z" }, + { url = "https://files.pythonhosted.org/packages/b5/a6/2a75320849dd154a793f69c951db759aedb8d1dd3939eeacda9bdcfa1629/pandas-3.0.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:356e5c055ed9b0da1580d465657bc7d00635af4fd47f30afb23025352ba764d1", size = 11405133, upload-time = "2026-02-17T22:18:48.533Z" }, + { url = "https://files.pythonhosted.org/packages/58/53/1d68fafb2e02d7881df66aa53be4cd748d25cbe311f3b3c85c93ea5d30ca/pandas-3.0.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:9d810036895f9ad6345b8f2a338dd6998a74e8483847403582cab67745bff821", size = 11932065, upload-time = "2026-02-17T22:18:50.837Z" }, + { url = "https://files.pythonhosted.org/packages/75/08/67cc404b3a966b6df27b38370ddd96b3b023030b572283d035181854aac5/pandas-3.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:536232a5fe26dd989bd633e7a0c450705fdc86a207fec7254a55e9a22950fe43", size = 9741627, upload-time = "2026-02-17T22:18:53.905Z" }, + { url = "https://files.pythonhosted.org/packages/86/4f/caf9952948fb00d23795f09b893d11f1cacb384e666854d87249530f7cbe/pandas-3.0.1-cp312-cp312-win_arm64.whl", hash = "sha256:0f463ebfd8de7f326d38037c7363c6dacb857c5881ab8961fb387804d6daf2f7", size = 9052483, upload-time = "2026-02-17T22:18:57.31Z" }, + { url = "https://files.pythonhosted.org/packages/0b/48/aad6ec4f8d007534c091e9a7172b3ec1b1ee6d99a9cbb936b5eab6c6cf58/pandas-3.0.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:5272627187b5d9c20e55d27caf5f2cd23e286aba25cadf73c8590e432e2b7262", size = 10317509, upload-time = "2026-02-17T22:18:59.498Z" }, + { url = "https://files.pythonhosted.org/packages/a8/14/5990826f779f79148ae9d3a2c39593dc04d61d5d90541e71b5749f35af95/pandas-3.0.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:661e0f665932af88c7877f31da0dc743fe9c8f2524bdffe23d24fdcb67ef9d56", size = 9860561, upload-time = "2026-02-17T22:19:02.265Z" }, + { url = "https://files.pythonhosted.org/packages/fa/80/f01ff54664b6d70fed71475543d108a9b7c888e923ad210795bef04ffb7d/pandas-3.0.1-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:75e6e292ff898679e47a2199172593d9f6107fd2dd3617c22c2946e97d5df46e", size = 10365506, upload-time = "2026-02-17T22:19:05.017Z" }, + { url = "https://files.pythonhosted.org/packages/f2/85/ab6d04733a7d6ff32bfc8382bf1b07078228f5d6ebec5266b91bfc5c4ff7/pandas-3.0.1-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1ff8cf1d2896e34343197685f432450ec99a85ba8d90cce2030c5eee2ef98791", size = 10873196, upload-time = "2026-02-17T22:19:07.204Z" }, + { url = "https://files.pythonhosted.org/packages/48/a9/9301c83d0b47c23ac5deab91c6b39fd98d5b5db4d93b25df8d381451828f/pandas-3.0.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:eca8b4510f6763f3d37359c2105df03a7a221a508f30e396a51d0713d462e68a", size = 11370859, upload-time = "2026-02-17T22:19:09.436Z" }, + { url = "https://files.pythonhosted.org/packages/59/fe/0c1fc5bd2d29c7db2ab372330063ad555fb83e08422829c785f5ec2176ca/pandas-3.0.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:06aff2ad6f0b94a17822cf8b83bbb563b090ed82ff4fe7712db2ce57cd50d9b8", size = 11924584, upload-time = "2026-02-17T22:19:11.562Z" }, + { url = "https://files.pythonhosted.org/packages/d6/7d/216a1588b65a7aa5f4535570418a599d943c85afb1d95b0876fc00aa1468/pandas-3.0.1-cp313-cp313-win_amd64.whl", hash = "sha256:9fea306c783e28884c29057a1d9baa11a349bbf99538ec1da44c8476563d1b25", size = 9742769, upload-time = "2026-02-17T22:19:13.926Z" }, + { url = "https://files.pythonhosted.org/packages/c4/cb/810a22a6af9a4e97c8ab1c946b47f3489c5bca5adc483ce0ffc84c9cc768/pandas-3.0.1-cp313-cp313-win_arm64.whl", hash = "sha256:a8d37a43c52917427e897cb2e429f67a449327394396a81034a4449b99afda59", size = 9043855, upload-time = "2026-02-17T22:19:16.09Z" }, + { url = "https://files.pythonhosted.org/packages/92/fa/423c89086cca1f039cf1253c3ff5b90f157b5b3757314aa635f6bf3e30aa/pandas-3.0.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:d54855f04f8246ed7b6fc96b05d4871591143c46c0b6f4af874764ed0d2d6f06", size = 10752673, upload-time = "2026-02-17T22:19:18.304Z" }, + { url = "https://files.pythonhosted.org/packages/22/23/b5a08ec1f40020397f0faba72f1e2c11f7596a6169c7b3e800abff0e433f/pandas-3.0.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:4e1b677accee34a09e0dc2ce5624e4a58a1870ffe56fc021e9caf7f23cd7668f", size = 10404967, upload-time = "2026-02-17T22:19:20.726Z" }, + { url = "https://files.pythonhosted.org/packages/5c/81/94841f1bb4afdc2b52a99daa895ac2c61600bb72e26525ecc9543d453ebc/pandas-3.0.1-cp313-cp313t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a9cabbdcd03f1b6cd254d6dda8ae09b0252524be1592594c00b7895916cb1324", size = 10320575, upload-time = "2026-02-17T22:19:24.919Z" }, + { url = "https://files.pythonhosted.org/packages/0a/8b/2ae37d66a5342a83adadfd0cb0b4bf9c3c7925424dd5f40d15d6cfaa35ee/pandas-3.0.1-cp313-cp313t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5ae2ab1f166668b41e770650101e7090824fd34d17915dd9cd479f5c5e0065e9", size = 10710921, upload-time = "2026-02-17T22:19:27.181Z" }, + { url = "https://files.pythonhosted.org/packages/a2/61/772b2e2757855e232b7ccf7cb8079a5711becb3a97f291c953def15a833f/pandas-3.0.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:6bf0603c2e30e2cafac32807b06435f28741135cb8697eae8b28c7d492fc7d76", size = 11334191, upload-time = "2026-02-17T22:19:29.411Z" }, + { url = "https://files.pythonhosted.org/packages/1b/08/b16c6df3ef555d8495d1d265a7963b65be166785d28f06a350913a4fac78/pandas-3.0.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:6c426422973973cae1f4a23e51d4ae85974f44871b24844e4f7de752dd877098", size = 11782256, upload-time = "2026-02-17T22:19:32.34Z" }, + { url = "https://files.pythonhosted.org/packages/55/80/178af0594890dee17e239fca96d3d8670ba0f5ff59b7d0439850924a9c09/pandas-3.0.1-cp313-cp313t-win_amd64.whl", hash = "sha256:b03f91ae8c10a85c1613102c7bef5229b5379f343030a3ccefeca8a33414cf35", size = 10485047, upload-time = "2026-02-17T22:19:34.605Z" }, + { url = "https://files.pythonhosted.org/packages/bb/8b/4bb774a998b97e6c2fd62a9e6cfdaae133b636fd1c468f92afb4ae9a447a/pandas-3.0.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:99d0f92ed92d3083d140bf6b97774f9f13863924cf3f52a70711f4e7588f9d0a", size = 10322465, upload-time = "2026-02-17T22:19:36.803Z" }, + { url = "https://files.pythonhosted.org/packages/72/3a/5b39b51c64159f470f1ca3b1c2a87da290657ca022f7cd11442606f607d1/pandas-3.0.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:3b66857e983208654294bb6477b8a63dee26b37bdd0eb34d010556e91261784f", size = 9910632, upload-time = "2026-02-17T22:19:39.001Z" }, + { url = "https://files.pythonhosted.org/packages/4e/f7/b449ffb3f68c11da12fc06fbf6d2fa3a41c41e17d0284d23a79e1c13a7e4/pandas-3.0.1-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:56cf59638bf24dc9bdf2154c81e248b3289f9a09a6d04e63608c159022352749", size = 10440535, upload-time = "2026-02-17T22:19:41.157Z" }, + { url = "https://files.pythonhosted.org/packages/55/77/6ea82043db22cb0f2bbfe7198da3544000ddaadb12d26be36e19b03a2dc5/pandas-3.0.1-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c1a9f55e0f46951874b863d1f3906dcb57df2d9be5c5847ba4dfb55b2c815249", size = 10893940, upload-time = "2026-02-17T22:19:43.493Z" }, + { url = "https://files.pythonhosted.org/packages/03/30/f1b502a72468c89412c1b882a08f6eed8a4ee9dc033f35f65d0663df6081/pandas-3.0.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:1849f0bba9c8a2fb0f691d492b834cc8dadf617e29015c66e989448d58d011ee", size = 11442711, upload-time = "2026-02-17T22:19:46.074Z" }, + { url = "https://files.pythonhosted.org/packages/0d/f0/ebb6ddd8fc049e98cabac5c2924d14d1dda26a20adb70d41ea2e428d3ec4/pandas-3.0.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:c3d288439e11b5325b02ae6e9cc83e6805a62c40c5a6220bea9beb899c073b1c", size = 11963918, upload-time = "2026-02-17T22:19:48.838Z" }, + { url = "https://files.pythonhosted.org/packages/09/f8/8ce132104074f977f907442790eaae24e27bce3b3b454e82faa3237ff098/pandas-3.0.1-cp314-cp314-win_amd64.whl", hash = "sha256:93325b0fe372d192965f4cca88d97667f49557398bbf94abdda3bf1b591dbe66", size = 9862099, upload-time = "2026-02-17T22:19:51.081Z" }, + { url = "https://files.pythonhosted.org/packages/e6/b7/6af9aac41ef2456b768ef0ae60acf8abcebb450a52043d030a65b4b7c9bd/pandas-3.0.1-cp314-cp314-win_arm64.whl", hash = "sha256:97ca08674e3287c7148f4858b01136f8bdfe7202ad25ad04fec602dd1d29d132", size = 9185333, upload-time = "2026-02-17T22:19:53.266Z" }, + { url = "https://files.pythonhosted.org/packages/66/fc/848bb6710bc6061cb0c5badd65b92ff75c81302e0e31e496d00029fe4953/pandas-3.0.1-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:58eeb1b2e0fb322befcf2bbc9ba0af41e616abadb3d3414a6bc7167f6cbfce32", size = 10772664, upload-time = "2026-02-17T22:19:55.806Z" }, + { url = "https://files.pythonhosted.org/packages/69/5c/866a9bbd0f79263b4b0db6ec1a341be13a1473323f05c122388e0f15b21d/pandas-3.0.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:cd9af1276b5ca9e298bd79a26bda32fa9cc87ed095b2a9a60978d2ca058eaf87", size = 10421286, upload-time = "2026-02-17T22:19:58.091Z" }, + { url = "https://files.pythonhosted.org/packages/51/a4/2058fb84fb1cfbfb2d4a6d485e1940bb4ad5716e539d779852494479c580/pandas-3.0.1-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:94f87a04984d6b63788327cd9f79dda62b7f9043909d2440ceccf709249ca988", size = 10342050, upload-time = "2026-02-17T22:20:01.376Z" }, + { url = "https://files.pythonhosted.org/packages/22/1b/674e89996cc4be74db3c4eb09240c4bb549865c9c3f5d9b086ff8fcfbf00/pandas-3.0.1-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:85fe4c4df62e1e20f9db6ebfb88c844b092c22cd5324bdcf94bfa2fc1b391221", size = 10740055, upload-time = "2026-02-17T22:20:04.328Z" }, + { url = "https://files.pythonhosted.org/packages/d0/f8/e954b750764298c22fa4614376531fe63c521ef517e7059a51f062b87dca/pandas-3.0.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:331ca75a2f8672c365ae25c0b29e46f5ac0c6551fdace8eec4cd65e4fac271ff", size = 11357632, upload-time = "2026-02-17T22:20:06.647Z" }, + { url = "https://files.pythonhosted.org/packages/6d/02/c6e04b694ffd68568297abd03588b6d30295265176a5c01b7459d3bc35a3/pandas-3.0.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:15860b1fdb1973fffade772fdb931ccf9b2f400a3f5665aef94a00445d7d8dd5", size = 11810974, upload-time = "2026-02-17T22:20:08.946Z" }, + { url = "https://files.pythonhosted.org/packages/89/41/d7dfb63d2407f12055215070c42fc6ac41b66e90a2946cdc5e759058398b/pandas-3.0.1-cp314-cp314t-win_amd64.whl", hash = "sha256:44f1364411d5670efa692b146c748f4ed013df91ee91e9bec5677fb1fd58b937", size = 10884622, upload-time = "2026-02-17T22:20:11.711Z" }, + { url = "https://files.pythonhosted.org/packages/68/b0/34937815889fa982613775e4b97fddd13250f11012d769949c5465af2150/pandas-3.0.1-cp314-cp314t-win_arm64.whl", hash = "sha256:108dd1790337a494aa80e38def654ca3f0968cf4f362c85f44c15e471667102d", size = 9452085, upload-time = "2026-02-17T22:20:14.331Z" }, +] + +[[package]] +name = "pluggy" +version = "1.6.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/f9/e2/3e91f31a7d2b083fe6ef3fa267035b518369d9511ffab804f839851d2779/pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3", size = 69412, upload-time = "2025-05-15T12:30:07.975Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746", size = 20538, upload-time = "2025-05-15T12:30:06.134Z" }, +] + +[[package]] +name = "pygments" +version = "2.19.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/b0/77/a5b8c569bf593b0140bde72ea885a803b82086995367bf2037de0159d924/pygments-2.19.2.tar.gz", hash = "sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887", size = 4968631, upload-time = "2025-06-21T13:39:12.283Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b", size = 1225217, upload-time = "2025-06-21T13:39:07.939Z" }, +] + +[[package]] +name = "pytest" +version = "9.0.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "colorama", marker = "sys_platform == 'win32'" }, + { name = "iniconfig" }, + { name = "packaging" }, + { name = "pluggy" }, + { name = "pygments" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/d1/db/7ef3487e0fb0049ddb5ce41d3a49c235bf9ad299b6a25d5780a89f19230f/pytest-9.0.2.tar.gz", hash = "sha256:75186651a92bd89611d1d9fc20f0b4345fd827c41ccd5c299a868a05d70edf11", size = 1568901, upload-time = "2025-12-06T21:30:51.014Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3b/ab/b3226f0bd7cdcf710fbede2b3548584366da3b19b5021e74f5bde2a8fa3f/pytest-9.0.2-py3-none-any.whl", hash = "sha256:711ffd45bf766d5264d487b917733b453d917afd2b0ad65223959f59089f875b", size = 374801, upload-time = "2025-12-06T21:30:49.154Z" }, +] + +[[package]] +name = "python-dateutil" +version = "2.9.0.post0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "six" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432, upload-time = "2024-03-01T18:36:20.211Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892, upload-time = "2024-03-01T18:36:18.57Z" }, +] + +[[package]] +name = "ruff" +version = "0.15.5" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/77/9b/840e0039e65fcf12758adf684d2289024d6140cde9268cc59887dc55189c/ruff-0.15.5.tar.gz", hash = "sha256:7c3601d3b6d76dce18c5c824fc8d06f4eef33d6df0c21ec7799510cde0f159a2", size = 4574214, upload-time = "2026-03-05T20:06:34.946Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/47/20/5369c3ce21588c708bcbe517a8fbe1a8dfdb5dfd5137e14790b1da71612c/ruff-0.15.5-py3-none-linux_armv6l.whl", hash = "sha256:4ae44c42281f42e3b06b988e442d344a5b9b72450ff3c892e30d11b29a96a57c", size = 10478185, upload-time = "2026-03-05T20:06:29.093Z" }, + { url = "https://files.pythonhosted.org/packages/44/ed/e81dd668547da281e5dce710cf0bc60193f8d3d43833e8241d006720e42b/ruff-0.15.5-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:6edd3792d408ebcf61adabc01822da687579a1a023f297618ac27a5b51ef0080", size = 10859201, upload-time = "2026-03-05T20:06:32.632Z" }, + { url = "https://files.pythonhosted.org/packages/c4/8f/533075f00aaf19b07c5cd6aa6e5d89424b06b3b3f4583bfa9c640a079059/ruff-0.15.5-py3-none-macosx_11_0_arm64.whl", hash = "sha256:89f463f7c8205a9f8dea9d658d59eff49db05f88f89cc3047fb1a02d9f344010", size = 10184752, upload-time = "2026-03-05T20:06:40.312Z" }, + { url = "https://files.pythonhosted.org/packages/66/0e/ba49e2c3fa0395b3152bad634c7432f7edfc509c133b8f4529053ff024fb/ruff-0.15.5-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ba786a8295c6574c1116704cf0b9e6563de3432ac888d8f83685654fe528fd65", size = 10534857, upload-time = "2026-03-05T20:06:19.581Z" }, + { url = "https://files.pythonhosted.org/packages/59/71/39234440f27a226475a0659561adb0d784b4d247dfe7f43ffc12dd02e288/ruff-0.15.5-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:fd4b801e57955fe9f02b31d20375ab3a5c4415f2e5105b79fb94cf2642c91440", size = 10309120, upload-time = "2026-03-05T20:06:00.435Z" }, + { url = "https://files.pythonhosted.org/packages/f5/87/4140aa86a93df032156982b726f4952aaec4a883bb98cb6ef73c347da253/ruff-0.15.5-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:391f7c73388f3d8c11b794dbbc2959a5b5afe66642c142a6effa90b45f6f5204", size = 11047428, upload-time = "2026-03-05T20:05:51.867Z" }, + { url = "https://files.pythonhosted.org/packages/5a/f7/4953e7e3287676f78fbe85e3a0ca414c5ca81237b7575bdadc00229ac240/ruff-0.15.5-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8dc18f30302e379fe1e998548b0f5e9f4dff907f52f73ad6da419ea9c19d66c8", size = 11914251, upload-time = "2026-03-05T20:06:22.887Z" }, + { url = "https://files.pythonhosted.org/packages/77/46/0f7c865c10cf896ccf5a939c3e84e1cfaeed608ff5249584799a74d33835/ruff-0.15.5-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1cc6e7f90087e2d27f98dc34ed1b3ab7c8f0d273cc5431415454e22c0bd2a681", size = 11333801, upload-time = "2026-03-05T20:05:57.168Z" }, + { url = "https://files.pythonhosted.org/packages/d3/01/a10fe54b653061585e655f5286c2662ebddb68831ed3eaebfb0eb08c0a16/ruff-0.15.5-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c1cb7169f53c1ddb06e71a9aebd7e98fc0fea936b39afb36d8e86d36ecc2636a", size = 11206821, upload-time = "2026-03-05T20:06:03.441Z" }, + { url = "https://files.pythonhosted.org/packages/7a/0d/2132ceaf20c5e8699aa83da2706ecb5c5dcdf78b453f77edca7fb70f8a93/ruff-0.15.5-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:9b037924500a31ee17389b5c8c4d88874cc6ea8e42f12e9c61a3d754ff72f1ca", size = 11133326, upload-time = "2026-03-05T20:06:25.655Z" }, + { url = "https://files.pythonhosted.org/packages/72/cb/2e5259a7eb2a0f87c08c0fe5bf5825a1e4b90883a52685524596bfc93072/ruff-0.15.5-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:65bb414e5b4eadd95a8c1e4804f6772bbe8995889f203a01f77ddf2d790929dd", size = 10510820, upload-time = "2026-03-05T20:06:37.79Z" }, + { url = "https://files.pythonhosted.org/packages/ff/20/b67ce78f9e6c59ffbdb5b4503d0090e749b5f2d31b599b554698a80d861c/ruff-0.15.5-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:d20aa469ae3b57033519c559e9bc9cd9e782842e39be05b50e852c7c981fa01d", size = 10302395, upload-time = "2026-03-05T20:05:54.504Z" }, + { url = "https://files.pythonhosted.org/packages/5f/e5/719f1acccd31b720d477751558ed74e9c88134adcc377e5e886af89d3072/ruff-0.15.5-py3-none-musllinux_1_2_i686.whl", hash = "sha256:15388dd28c9161cdb8eda68993533acc870aa4e646a0a277aa166de9ad5a8752", size = 10754069, upload-time = "2026-03-05T20:06:06.422Z" }, + { url = "https://files.pythonhosted.org/packages/c3/9c/d1db14469e32d98f3ca27079dbd30b7b44dbb5317d06ab36718dee3baf03/ruff-0.15.5-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:b30da330cbd03bed0c21420b6b953158f60c74c54c5f4c1dabbdf3a57bf355d2", size = 11304315, upload-time = "2026-03-05T20:06:10.867Z" }, + { url = "https://files.pythonhosted.org/packages/28/3a/950367aee7c69027f4f422059227b290ed780366b6aecee5de5039d50fa8/ruff-0.15.5-py3-none-win32.whl", hash = "sha256:732e5ee1f98ba5b3679029989a06ca39a950cced52143a0ea82a2102cb592b74", size = 10551676, upload-time = "2026-03-05T20:06:13.705Z" }, + { url = "https://files.pythonhosted.org/packages/b8/00/bf077a505b4e649bdd3c47ff8ec967735ce2544c8e4a43aba42ee9bf935d/ruff-0.15.5-py3-none-win_amd64.whl", hash = "sha256:821d41c5fa9e19117616c35eaa3f4b75046ec76c65e7ae20a333e9a8696bc7fe", size = 11678972, upload-time = "2026-03-05T20:06:45.379Z" }, + { url = "https://files.pythonhosted.org/packages/fe/4e/cd76eca6db6115604b7626668e891c9dd03330384082e33662fb0f113614/ruff-0.15.5-py3-none-win_arm64.whl", hash = "sha256:b498d1c60d2fe5c10c45ec3f698901065772730b411f164ae270bb6bfcc4740b", size = 10965572, upload-time = "2026-03-05T20:06:16.984Z" }, +] + +[[package]] +name = "scikit-learn" +version = "1.8.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "joblib" }, + { name = "numpy" }, + { name = "scipy" }, + { name = "threadpoolctl" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/0e/d4/40988bf3b8e34feec1d0e6a051446b1f66225f8529b9309becaeef62b6c4/scikit_learn-1.8.0.tar.gz", hash = "sha256:9bccbb3b40e3de10351f8f5068e105d0f4083b1a65fa07b6634fbc401a6287fd", size = 7335585, upload-time = "2025-12-10T07:08:53.618Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c9/92/53ea2181da8ac6bf27170191028aee7251f8f841f8d3edbfdcaf2008fde9/scikit_learn-1.8.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:146b4d36f800c013d267b29168813f7a03a43ecd2895d04861f1240b564421da", size = 8595835, upload-time = "2025-12-10T07:07:39.385Z" }, + { url = "https://files.pythonhosted.org/packages/01/18/d154dc1638803adf987910cdd07097d9c526663a55666a97c124d09fb96a/scikit_learn-1.8.0-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:f984ca4b14914e6b4094c5d52a32ea16b49832c03bd17a110f004db3c223e8e1", size = 8080381, upload-time = "2025-12-10T07:07:41.93Z" }, + { url = "https://files.pythonhosted.org/packages/8a/44/226142fcb7b7101e64fdee5f49dbe6288d4c7af8abf593237b70fca080a4/scikit_learn-1.8.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5e30adb87f0cc81c7690a84f7932dd66be5bac57cfe16b91cb9151683a4a2d3b", size = 8799632, upload-time = "2025-12-10T07:07:43.899Z" }, + { url = "https://files.pythonhosted.org/packages/36/4d/4a67f30778a45d542bbea5db2dbfa1e9e100bf9ba64aefe34215ba9f11f6/scikit_learn-1.8.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ada8121bcb4dac28d930febc791a69f7cb1673c8495e5eee274190b73a4559c1", size = 9103788, upload-time = "2025-12-10T07:07:45.982Z" }, + { url = "https://files.pythonhosted.org/packages/89/3c/45c352094cfa60050bcbb967b1faf246b22e93cb459f2f907b600f2ceda5/scikit_learn-1.8.0-cp311-cp311-win_amd64.whl", hash = "sha256:c57b1b610bd1f40ba43970e11ce62821c2e6569e4d74023db19c6b26f246cb3b", size = 8081706, upload-time = "2025-12-10T07:07:48.111Z" }, + { url = "https://files.pythonhosted.org/packages/3d/46/5416595bb395757f754feb20c3d776553a386b661658fb21b7c814e89efe/scikit_learn-1.8.0-cp311-cp311-win_arm64.whl", hash = "sha256:2838551e011a64e3053ad7618dda9310175f7515f1742fa2d756f7c874c05961", size = 7688451, upload-time = "2025-12-10T07:07:49.873Z" }, + { url = "https://files.pythonhosted.org/packages/90/74/e6a7cc4b820e95cc38cf36cd74d5aa2b42e8ffc2d21fe5a9a9c45c1c7630/scikit_learn-1.8.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:5fb63362b5a7ddab88e52b6dbb47dac3fd7dafeee740dc6c8d8a446ddedade8e", size = 8548242, upload-time = "2025-12-10T07:07:51.568Z" }, + { url = "https://files.pythonhosted.org/packages/49/d8/9be608c6024d021041c7f0b3928d4749a706f4e2c3832bbede4fb4f58c95/scikit_learn-1.8.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:5025ce924beccb28298246e589c691fe1b8c1c96507e6d27d12c5fadd85bfd76", size = 8079075, upload-time = "2025-12-10T07:07:53.697Z" }, + { url = "https://files.pythonhosted.org/packages/dd/47/f187b4636ff80cc63f21cd40b7b2d177134acaa10f6bb73746130ee8c2e5/scikit_learn-1.8.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4496bb2cf7a43ce1a2d7524a79e40bc5da45cf598dbf9545b7e8316ccba47bb4", size = 8660492, upload-time = "2025-12-10T07:07:55.574Z" }, + { url = "https://files.pythonhosted.org/packages/97/74/b7a304feb2b49df9fafa9382d4d09061a96ee9a9449a7cbea7988dda0828/scikit_learn-1.8.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a0bcfe4d0d14aec44921545fd2af2338c7471de9cb701f1da4c9d85906ab847a", size = 8931904, upload-time = "2025-12-10T07:07:57.666Z" }, + { url = "https://files.pythonhosted.org/packages/9f/c4/0ab22726a04ede56f689476b760f98f8f46607caecff993017ac1b64aa5d/scikit_learn-1.8.0-cp312-cp312-win_amd64.whl", hash = "sha256:35c007dedb2ffe38fe3ee7d201ebac4a2deccd2408e8621d53067733e3c74809", size = 8019359, upload-time = "2025-12-10T07:07:59.838Z" }, + { url = "https://files.pythonhosted.org/packages/24/90/344a67811cfd561d7335c1b96ca21455e7e472d281c3c279c4d3f2300236/scikit_learn-1.8.0-cp312-cp312-win_arm64.whl", hash = "sha256:8c497fff237d7b4e07e9ef1a640887fa4fb765647f86fbe00f969ff6280ce2bb", size = 7641898, upload-time = "2025-12-10T07:08:01.36Z" }, + { url = "https://files.pythonhosted.org/packages/03/aa/e22e0768512ce9255eba34775be2e85c2048da73da1193e841707f8f039c/scikit_learn-1.8.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0d6ae97234d5d7079dc0040990a6f7aeb97cb7fa7e8945f1999a429b23569e0a", size = 8513770, upload-time = "2025-12-10T07:08:03.251Z" }, + { url = "https://files.pythonhosted.org/packages/58/37/31b83b2594105f61a381fc74ca19e8780ee923be2d496fcd8d2e1147bd99/scikit_learn-1.8.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:edec98c5e7c128328124a029bceb09eda2d526997780fef8d65e9a69eead963e", size = 8044458, upload-time = "2025-12-10T07:08:05.336Z" }, + { url = "https://files.pythonhosted.org/packages/2d/5a/3f1caed8765f33eabb723596666da4ebbf43d11e96550fb18bdec42b467b/scikit_learn-1.8.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:74b66d8689d52ed04c271e1329f0c61635bcaf5b926db9b12d58914cdc01fe57", size = 8610341, upload-time = "2025-12-10T07:08:07.732Z" }, + { url = "https://files.pythonhosted.org/packages/38/cf/06896db3f71c75902a8e9943b444a56e727418f6b4b4a90c98c934f51ed4/scikit_learn-1.8.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8fdf95767f989b0cfedb85f7ed8ca215d4be728031f56ff5a519ee1e3276dc2e", size = 8900022, upload-time = "2025-12-10T07:08:09.862Z" }, + { url = "https://files.pythonhosted.org/packages/1c/f9/9b7563caf3ec8873e17a31401858efab6b39a882daf6c1bfa88879c0aa11/scikit_learn-1.8.0-cp313-cp313-win_amd64.whl", hash = "sha256:2de443b9373b3b615aec1bb57f9baa6bb3a9bd093f1269ba95c17d870422b271", size = 7989409, upload-time = "2025-12-10T07:08:12.028Z" }, + { url = "https://files.pythonhosted.org/packages/49/bd/1f4001503650e72c4f6009ac0c4413cb17d2d601cef6f71c0453da2732fc/scikit_learn-1.8.0-cp313-cp313-win_arm64.whl", hash = "sha256:eddde82a035681427cbedded4e6eff5e57fa59216c2e3e90b10b19ab1d0a65c3", size = 7619760, upload-time = "2025-12-10T07:08:13.688Z" }, + { url = "https://files.pythonhosted.org/packages/d2/7d/a630359fc9dcc95496588c8d8e3245cc8fd81980251079bc09c70d41d951/scikit_learn-1.8.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:7cc267b6108f0a1499a734167282c00c4ebf61328566b55ef262d48e9849c735", size = 8826045, upload-time = "2025-12-10T07:08:15.215Z" }, + { url = "https://files.pythonhosted.org/packages/cc/56/a0c86f6930cfcd1c7054a2bc417e26960bb88d32444fe7f71d5c2cfae891/scikit_learn-1.8.0-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:fe1c011a640a9f0791146011dfd3c7d9669785f9fed2b2a5f9e207536cf5c2fd", size = 8420324, upload-time = "2025-12-10T07:08:17.561Z" }, + { url = "https://files.pythonhosted.org/packages/46/1e/05962ea1cebc1cf3876667ecb14c283ef755bf409993c5946ade3b77e303/scikit_learn-1.8.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:72358cce49465d140cc4e7792015bb1f0296a9742d5622c67e31399b75468b9e", size = 8680651, upload-time = "2025-12-10T07:08:19.952Z" }, + { url = "https://files.pythonhosted.org/packages/fe/56/a85473cd75f200c9759e3a5f0bcab2d116c92a8a02ee08ccd73b870f8bb4/scikit_learn-1.8.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:80832434a6cc114f5219211eec13dcbc16c2bac0e31ef64c6d346cde3cf054cb", size = 8925045, upload-time = "2025-12-10T07:08:22.11Z" }, + { url = "https://files.pythonhosted.org/packages/cc/b7/64d8cfa896c64435ae57f4917a548d7ac7a44762ff9802f75a79b77cb633/scikit_learn-1.8.0-cp313-cp313t-win_amd64.whl", hash = "sha256:ee787491dbfe082d9c3013f01f5991658b0f38aa8177e4cd4bf434c58f551702", size = 8507994, upload-time = "2025-12-10T07:08:23.943Z" }, + { url = "https://files.pythonhosted.org/packages/5e/37/e192ea709551799379958b4c4771ec507347027bb7c942662c7fbeba31cb/scikit_learn-1.8.0-cp313-cp313t-win_arm64.whl", hash = "sha256:bf97c10a3f5a7543f9b88cbf488d33d175e9146115a451ae34568597ba33dcde", size = 7869518, upload-time = "2025-12-10T07:08:25.71Z" }, + { url = "https://files.pythonhosted.org/packages/24/05/1af2c186174cc92dcab2233f327336058c077d38f6fe2aceb08e6ab4d509/scikit_learn-1.8.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:c22a2da7a198c28dd1a6e1136f19c830beab7fdca5b3e5c8bba8394f8a5c45b3", size = 8528667, upload-time = "2025-12-10T07:08:27.541Z" }, + { url = "https://files.pythonhosted.org/packages/a8/25/01c0af38fe969473fb292bba9dc2b8f9b451f3112ff242c647fee3d0dfe7/scikit_learn-1.8.0-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:6b595b07a03069a2b1740dc08c2299993850ea81cce4fe19b2421e0c970de6b7", size = 8066524, upload-time = "2025-12-10T07:08:29.822Z" }, + { url = "https://files.pythonhosted.org/packages/be/ce/a0623350aa0b68647333940ee46fe45086c6060ec604874e38e9ab7d8e6c/scikit_learn-1.8.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:29ffc74089f3d5e87dfca4c2c8450f88bdc61b0fc6ed5d267f3988f19a1309f6", size = 8657133, upload-time = "2025-12-10T07:08:31.865Z" }, + { url = "https://files.pythonhosted.org/packages/b8/cb/861b41341d6f1245e6ca80b1c1a8c4dfce43255b03df034429089ca2a2c5/scikit_learn-1.8.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fb65db5d7531bccf3a4f6bec3462223bea71384e2cda41da0f10b7c292b9e7c4", size = 8923223, upload-time = "2025-12-10T07:08:34.166Z" }, + { url = "https://files.pythonhosted.org/packages/76/18/a8def8f91b18cd1ba6e05dbe02540168cb24d47e8dcf69e8d00b7da42a08/scikit_learn-1.8.0-cp314-cp314-win_amd64.whl", hash = "sha256:56079a99c20d230e873ea40753102102734c5953366972a71d5cb39a32bc40c6", size = 8096518, upload-time = "2025-12-10T07:08:36.339Z" }, + { url = "https://files.pythonhosted.org/packages/d1/77/482076a678458307f0deb44e29891d6022617b2a64c840c725495bee343f/scikit_learn-1.8.0-cp314-cp314-win_arm64.whl", hash = "sha256:3bad7565bc9cf37ce19a7c0d107742b320c1285df7aab1a6e2d28780df167242", size = 7754546, upload-time = "2025-12-10T07:08:38.128Z" }, + { url = "https://files.pythonhosted.org/packages/2d/d1/ef294ca754826daa043b2a104e59960abfab4cf653891037d19dd5b6f3cf/scikit_learn-1.8.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:4511be56637e46c25721e83d1a9cea9614e7badc7040c4d573d75fbe257d6fd7", size = 8848305, upload-time = "2025-12-10T07:08:41.013Z" }, + { url = "https://files.pythonhosted.org/packages/5b/e2/b1f8b05138ee813b8e1a4149f2f0d289547e60851fd1bb268886915adbda/scikit_learn-1.8.0-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:a69525355a641bf8ef136a7fa447672fb54fe8d60cab5538d9eb7c6438543fb9", size = 8432257, upload-time = "2025-12-10T07:08:42.873Z" }, + { url = "https://files.pythonhosted.org/packages/26/11/c32b2138a85dcb0c99f6afd13a70a951bfdff8a6ab42d8160522542fb647/scikit_learn-1.8.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c2656924ec73e5939c76ac4c8b026fc203b83d8900362eb2599d8aee80e4880f", size = 8678673, upload-time = "2025-12-10T07:08:45.362Z" }, + { url = "https://files.pythonhosted.org/packages/c7/57/51f2384575bdec454f4fe4e7a919d696c9ebce914590abf3e52d47607ab8/scikit_learn-1.8.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:15fc3b5d19cc2be65404786857f2e13c70c83dd4782676dd6814e3b89dc8f5b9", size = 8922467, upload-time = "2025-12-10T07:08:47.408Z" }, + { url = "https://files.pythonhosted.org/packages/35/4d/748c9e2872637a57981a04adc038dacaa16ba8ca887b23e34953f0b3f742/scikit_learn-1.8.0-cp314-cp314t-win_amd64.whl", hash = "sha256:00d6f1d66fbcf4eba6e356e1420d33cc06c70a45bb1363cd6f6a8e4ebbbdece2", size = 8774395, upload-time = "2025-12-10T07:08:49.337Z" }, + { url = "https://files.pythonhosted.org/packages/60/22/d7b2ebe4704a5e50790ba089d5c2ae308ab6bb852719e6c3bd4f04c3a363/scikit_learn-1.8.0-cp314-cp314t-win_arm64.whl", hash = "sha256:f28dd15c6bb0b66ba09728cf09fd8736c304be29409bd8445a080c1280619e8c", size = 8002647, upload-time = "2025-12-10T07:08:51.601Z" }, +] + +[[package]] +name = "scipy" +version = "1.15.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "numpy" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/0f/37/6964b830433e654ec7485e45a00fc9a27cf868d622838f6b6d9c5ec0d532/scipy-1.15.3.tar.gz", hash = "sha256:eae3cf522bc7df64b42cad3925c876e1b0b6c35c1337c93e12c0f366f55b0eaf", size = 59419214, upload-time = "2025-05-08T16:13:05.955Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/96/ab/5cc9f80f28f6a7dff646c5756e559823614a42b1939d86dd0ed550470210/scipy-1.15.3-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:993439ce220d25e3696d1b23b233dd010169b62f6456488567e830654ee37a6b", size = 38714255, upload-time = "2025-05-08T16:05:14.596Z" }, + { url = "https://files.pythonhosted.org/packages/4a/4a/66ba30abe5ad1a3ad15bfb0b59d22174012e8056ff448cb1644deccbfed2/scipy-1.15.3-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:34716e281f181a02341ddeaad584205bd2fd3c242063bd3423d61ac259ca7eba", size = 30111035, upload-time = "2025-05-08T16:05:20.152Z" }, + { url = "https://files.pythonhosted.org/packages/4b/fa/a7e5b95afd80d24313307f03624acc65801846fa75599034f8ceb9e2cbf6/scipy-1.15.3-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:3b0334816afb8b91dab859281b1b9786934392aa3d527cd847e41bb6f45bee65", size = 22384499, upload-time = "2025-05-08T16:05:24.494Z" }, + { url = "https://files.pythonhosted.org/packages/17/99/f3aaddccf3588bb4aea70ba35328c204cadd89517a1612ecfda5b2dd9d7a/scipy-1.15.3-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:6db907c7368e3092e24919b5e31c76998b0ce1684d51a90943cb0ed1b4ffd6c1", size = 25152602, upload-time = "2025-05-08T16:05:29.313Z" }, + { url = "https://files.pythonhosted.org/packages/56/c5/1032cdb565f146109212153339f9cb8b993701e9fe56b1c97699eee12586/scipy-1.15.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:721d6b4ef5dc82ca8968c25b111e307083d7ca9091bc38163fb89243e85e3889", size = 35503415, upload-time = "2025-05-08T16:05:34.699Z" }, + { url = "https://files.pythonhosted.org/packages/bd/37/89f19c8c05505d0601ed5650156e50eb881ae3918786c8fd7262b4ee66d3/scipy-1.15.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:39cb9c62e471b1bb3750066ecc3a3f3052b37751c7c3dfd0fd7e48900ed52982", size = 37652622, upload-time = "2025-05-08T16:05:40.762Z" }, + { url = "https://files.pythonhosted.org/packages/7e/31/be59513aa9695519b18e1851bb9e487de66f2d31f835201f1b42f5d4d475/scipy-1.15.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:795c46999bae845966368a3c013e0e00947932d68e235702b5c3f6ea799aa8c9", size = 37244796, upload-time = "2025-05-08T16:05:48.119Z" }, + { url = "https://files.pythonhosted.org/packages/10/c0/4f5f3eeccc235632aab79b27a74a9130c6c35df358129f7ac8b29f562ac7/scipy-1.15.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:18aaacb735ab38b38db42cb01f6b92a2d0d4b6aabefeb07f02849e47f8fb3594", size = 40047684, upload-time = "2025-05-08T16:05:54.22Z" }, + { url = "https://files.pythonhosted.org/packages/ab/a7/0ddaf514ce8a8714f6ed243a2b391b41dbb65251affe21ee3077ec45ea9a/scipy-1.15.3-cp311-cp311-win_amd64.whl", hash = "sha256:ae48a786a28412d744c62fd7816a4118ef97e5be0bee968ce8f0a2fba7acf3bb", size = 41246504, upload-time = "2025-05-08T16:06:00.437Z" }, + { url = "https://files.pythonhosted.org/packages/37/4b/683aa044c4162e10ed7a7ea30527f2cbd92e6999c10a8ed8edb253836e9c/scipy-1.15.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:6ac6310fdbfb7aa6612408bd2f07295bcbd3fda00d2d702178434751fe48e019", size = 38766735, upload-time = "2025-05-08T16:06:06.471Z" }, + { url = "https://files.pythonhosted.org/packages/7b/7e/f30be3d03de07f25dc0ec926d1681fed5c732d759ac8f51079708c79e680/scipy-1.15.3-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:185cd3d6d05ca4b44a8f1595af87f9c372bb6acf9c808e99aa3e9aa03bd98cf6", size = 30173284, upload-time = "2025-05-08T16:06:11.686Z" }, + { url = "https://files.pythonhosted.org/packages/07/9c/0ddb0d0abdabe0d181c1793db51f02cd59e4901da6f9f7848e1f96759f0d/scipy-1.15.3-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:05dc6abcd105e1a29f95eada46d4a3f251743cfd7d3ae8ddb4088047f24ea477", size = 22446958, upload-time = "2025-05-08T16:06:15.97Z" }, + { url = "https://files.pythonhosted.org/packages/af/43/0bce905a965f36c58ff80d8bea33f1f9351b05fad4beaad4eae34699b7a1/scipy-1.15.3-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:06efcba926324df1696931a57a176c80848ccd67ce6ad020c810736bfd58eb1c", size = 25242454, upload-time = "2025-05-08T16:06:20.394Z" }, + { url = "https://files.pythonhosted.org/packages/56/30/a6f08f84ee5b7b28b4c597aca4cbe545535c39fe911845a96414700b64ba/scipy-1.15.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c05045d8b9bfd807ee1b9f38761993297b10b245f012b11b13b91ba8945f7e45", size = 35210199, upload-time = "2025-05-08T16:06:26.159Z" }, + { url = "https://files.pythonhosted.org/packages/0b/1f/03f52c282437a168ee2c7c14a1a0d0781a9a4a8962d84ac05c06b4c5b555/scipy-1.15.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:271e3713e645149ea5ea3e97b57fdab61ce61333f97cfae392c28ba786f9bb49", size = 37309455, upload-time = "2025-05-08T16:06:32.778Z" }, + { url = "https://files.pythonhosted.org/packages/89/b1/fbb53137f42c4bf630b1ffdfc2151a62d1d1b903b249f030d2b1c0280af8/scipy-1.15.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:6cfd56fc1a8e53f6e89ba3a7a7251f7396412d655bca2aa5611c8ec9a6784a1e", size = 36885140, upload-time = "2025-05-08T16:06:39.249Z" }, + { url = "https://files.pythonhosted.org/packages/2e/2e/025e39e339f5090df1ff266d021892694dbb7e63568edcfe43f892fa381d/scipy-1.15.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:0ff17c0bb1cb32952c09217d8d1eed9b53d1463e5f1dd6052c7857f83127d539", size = 39710549, upload-time = "2025-05-08T16:06:45.729Z" }, + { url = "https://files.pythonhosted.org/packages/e6/eb/3bf6ea8ab7f1503dca3a10df2e4b9c3f6b3316df07f6c0ded94b281c7101/scipy-1.15.3-cp312-cp312-win_amd64.whl", hash = "sha256:52092bc0472cfd17df49ff17e70624345efece4e1a12b23783a1ac59a1b728ed", size = 40966184, upload-time = "2025-05-08T16:06:52.623Z" }, + { url = "https://files.pythonhosted.org/packages/73/18/ec27848c9baae6e0d6573eda6e01a602e5649ee72c27c3a8aad673ebecfd/scipy-1.15.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:2c620736bcc334782e24d173c0fdbb7590a0a436d2fdf39310a8902505008759", size = 38728256, upload-time = "2025-05-08T16:06:58.696Z" }, + { url = "https://files.pythonhosted.org/packages/74/cd/1aef2184948728b4b6e21267d53b3339762c285a46a274ebb7863c9e4742/scipy-1.15.3-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:7e11270a000969409d37ed399585ee530b9ef6aa99d50c019de4cb01e8e54e62", size = 30109540, upload-time = "2025-05-08T16:07:04.209Z" }, + { url = "https://files.pythonhosted.org/packages/5b/d8/59e452c0a255ec352bd0a833537a3bc1bfb679944c4938ab375b0a6b3a3e/scipy-1.15.3-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:8c9ed3ba2c8a2ce098163a9bdb26f891746d02136995df25227a20e71c396ebb", size = 22383115, upload-time = "2025-05-08T16:07:08.998Z" }, + { url = "https://files.pythonhosted.org/packages/08/f5/456f56bbbfccf696263b47095291040655e3cbaf05d063bdc7c7517f32ac/scipy-1.15.3-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:0bdd905264c0c9cfa74a4772cdb2070171790381a5c4d312c973382fc6eaf730", size = 25163884, upload-time = "2025-05-08T16:07:14.091Z" }, + { url = "https://files.pythonhosted.org/packages/a2/66/a9618b6a435a0f0c0b8a6d0a2efb32d4ec5a85f023c2b79d39512040355b/scipy-1.15.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:79167bba085c31f38603e11a267d862957cbb3ce018d8b38f79ac043bc92d825", size = 35174018, upload-time = "2025-05-08T16:07:19.427Z" }, + { url = "https://files.pythonhosted.org/packages/b5/09/c5b6734a50ad4882432b6bb7c02baf757f5b2f256041da5df242e2d7e6b6/scipy-1.15.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c9deabd6d547aee2c9a81dee6cc96c6d7e9a9b1953f74850c179f91fdc729cb7", size = 37269716, upload-time = "2025-05-08T16:07:25.712Z" }, + { url = "https://files.pythonhosted.org/packages/77/0a/eac00ff741f23bcabd352731ed9b8995a0a60ef57f5fd788d611d43d69a1/scipy-1.15.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:dde4fc32993071ac0c7dd2d82569e544f0bdaff66269cb475e0f369adad13f11", size = 36872342, upload-time = "2025-05-08T16:07:31.468Z" }, + { url = "https://files.pythonhosted.org/packages/fe/54/4379be86dd74b6ad81551689107360d9a3e18f24d20767a2d5b9253a3f0a/scipy-1.15.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:f77f853d584e72e874d87357ad70f44b437331507d1c311457bed8ed2b956126", size = 39670869, upload-time = "2025-05-08T16:07:38.002Z" }, + { url = "https://files.pythonhosted.org/packages/87/2e/892ad2862ba54f084ffe8cc4a22667eaf9c2bcec6d2bff1d15713c6c0703/scipy-1.15.3-cp313-cp313-win_amd64.whl", hash = "sha256:b90ab29d0c37ec9bf55424c064312930ca5f4bde15ee8619ee44e69319aab163", size = 40988851, upload-time = "2025-05-08T16:08:33.671Z" }, + { url = "https://files.pythonhosted.org/packages/1b/e9/7a879c137f7e55b30d75d90ce3eb468197646bc7b443ac036ae3fe109055/scipy-1.15.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:3ac07623267feb3ae308487c260ac684b32ea35fd81e12845039952f558047b8", size = 38863011, upload-time = "2025-05-08T16:07:44.039Z" }, + { url = "https://files.pythonhosted.org/packages/51/d1/226a806bbd69f62ce5ef5f3ffadc35286e9fbc802f606a07eb83bf2359de/scipy-1.15.3-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:6487aa99c2a3d509a5227d9a5e889ff05830a06b2ce08ec30df6d79db5fcd5c5", size = 30266407, upload-time = "2025-05-08T16:07:49.891Z" }, + { url = "https://files.pythonhosted.org/packages/e5/9b/f32d1d6093ab9eeabbd839b0f7619c62e46cc4b7b6dbf05b6e615bbd4400/scipy-1.15.3-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:50f9e62461c95d933d5c5ef4a1f2ebf9a2b4e83b0db374cb3f1de104d935922e", size = 22540030, upload-time = "2025-05-08T16:07:54.121Z" }, + { url = "https://files.pythonhosted.org/packages/e7/29/c278f699b095c1a884f29fda126340fcc201461ee8bfea5c8bdb1c7c958b/scipy-1.15.3-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:14ed70039d182f411ffc74789a16df3835e05dc469b898233a245cdfd7f162cb", size = 25218709, upload-time = "2025-05-08T16:07:58.506Z" }, + { url = "https://files.pythonhosted.org/packages/24/18/9e5374b617aba742a990581373cd6b68a2945d65cc588482749ef2e64467/scipy-1.15.3-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0a769105537aa07a69468a0eefcd121be52006db61cdd8cac8a0e68980bbb723", size = 34809045, upload-time = "2025-05-08T16:08:03.929Z" }, + { url = "https://files.pythonhosted.org/packages/e1/fe/9c4361e7ba2927074360856db6135ef4904d505e9b3afbbcb073c4008328/scipy-1.15.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9db984639887e3dffb3928d118145ffe40eff2fa40cb241a306ec57c219ebbbb", size = 36703062, upload-time = "2025-05-08T16:08:09.558Z" }, + { url = "https://files.pythonhosted.org/packages/b7/8e/038ccfe29d272b30086b25a4960f757f97122cb2ec42e62b460d02fe98e9/scipy-1.15.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:40e54d5c7e7ebf1aa596c374c49fa3135f04648a0caabcb66c52884b943f02b4", size = 36393132, upload-time = "2025-05-08T16:08:15.34Z" }, + { url = "https://files.pythonhosted.org/packages/10/7e/5c12285452970be5bdbe8352c619250b97ebf7917d7a9a9e96b8a8140f17/scipy-1.15.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:5e721fed53187e71d0ccf382b6bf977644c533e506c4d33c3fb24de89f5c3ed5", size = 38979503, upload-time = "2025-05-08T16:08:21.513Z" }, + { url = "https://files.pythonhosted.org/packages/81/06/0a5e5349474e1cbc5757975b21bd4fad0e72ebf138c5592f191646154e06/scipy-1.15.3-cp313-cp313t-win_amd64.whl", hash = "sha256:76ad1fb5f8752eabf0fa02e4cc0336b4e8f021e2d5f061ed37d6d264db35e3ca", size = 40308097, upload-time = "2025-05-08T16:08:27.627Z" }, +] + +[[package]] +name = "six" +version = "1.17.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031, upload-time = "2024-12-04T17:35:28.174Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050, upload-time = "2024-12-04T17:35:26.475Z" }, +] + +[[package]] +name = "threadpoolctl" +version = "3.6.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/b7/4d/08c89e34946fce2aec4fbb45c9016efd5f4d7f24af8e5d93296e935631d8/threadpoolctl-3.6.0.tar.gz", hash = "sha256:8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e", size = 21274, upload-time = "2025-03-13T13:49:23.031Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl", hash = "sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb", size = 18638, upload-time = "2025-03-13T13:49:21.846Z" }, +] + +[[package]] +name = "tzdata" +version = "2025.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/5e/a7/c202b344c5ca7daf398f3b8a477eeb205cf3b6f32e7ec3a6bac0629ca975/tzdata-2025.3.tar.gz", hash = "sha256:de39c2ca5dc7b0344f2eba86f49d614019d29f060fc4ebc8a417896a620b56a7", size = 196772, upload-time = "2025-12-13T17:45:35.667Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c7/b0/003792df09decd6849a5e39c28b513c06e84436a54440380862b5aeff25d/tzdata-2025.3-py2.py3-none-any.whl", hash = "sha256:06a47e5700f3081aab02b2e513160914ff0694bce9947d6b76ebd6bf57cfc5d1", size = 348521, upload-time = "2025-12-13T17:45:33.889Z" }, +] + +[[package]] +name = "unsupervised-bias-detection" +version = "1.0.0" +source = { virtual = "." } +dependencies = [ + { name = "kmodes" }, + { name = "numpy" }, + { name = "scikit-learn" }, +] + +[package.dev-dependencies] +dev = [ + { name = "fairlearn" }, + { name = "pandas" }, + { name = "pytest" }, + { name = "ruff" }, +] + +[package.metadata] +requires-dist = [ + { name = "kmodes", specifier = ">=0.12.2" }, + { name = "numpy", specifier = ">=2.4.3" }, + { name = "scikit-learn", specifier = ">=1.8.0" }, +] + +[package.metadata.requires-dev] +dev = [ + { name = "fairlearn", specifier = ">=0.13.0" }, + { name = "pandas", specifier = ">=3.0.1" }, + { name = "pytest", specifier = ">=9.0.2" }, + { name = "ruff", specifier = ">=0.15.5" }, +] From 2ac4f7a1bbe38211ecd6be03d89e0c5e40d3b228 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Krsto=20Prorokovi=C4=87?= Date: Sat, 14 Mar 2026 17:59:24 +0100 Subject: [PATCH 3/5] Lint code --- unsupervised_bias_detection/cluster/_bahc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/unsupervised_bias_detection/cluster/_bahc.py b/unsupervised_bias_detection/cluster/_bahc.py index a95a1b1..bd5999c 100644 --- a/unsupervised_bias_detection/cluster/_bahc.py +++ b/unsupervised_bias_detection/cluster/_bahc.py @@ -1,5 +1,5 @@ -import heapq from collections import deque +import heapq from numbers import Integral, Real from typing import Any, ClassVar From 4a035f8241267ccdf88093910ed6360d1d6c50e4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Krsto=20Prorokovi=C4=87?= Date: Sat, 14 Mar 2026 18:05:40 +0100 Subject: [PATCH 4/5] Add Install build dependencies to ci.yml --- .github/workflows/ci.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 1ffb50e..c2bf707 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -25,6 +25,9 @@ jobs: python-version: ${{ matrix.python-version }} enable-cache: true + - name: Install build dependencies + run: sudo apt-get install -y gfortran libopenblas-dev pkg-config + - name: Install dependencies run: uv sync --locked From 514526fecdbf107b389182cef17e3a7e051b91dd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Krsto=20Prorokovi=C4=87?= Date: Sat, 14 Mar 2026 18:10:17 +0100 Subject: [PATCH 5/5] Make pytest run only doctests in cluster module for now --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index f0e6e4e..18c4679 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,7 +26,7 @@ dev = [ ] [tool.pytest.ini_options] -testpaths = ["tests", "unsupervised_bias_detection"] +testpaths = ["tests", "unsupervised_bias_detection/cluster"] addopts = "--verbose --showlocals --full-trace --color=yes --doctest-modules" [tool.ruff]