From b71ce9ed0561aaee3dae8f642e8bb168c29d39ab Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 16 Jul 2026 19:08:06 -0600 Subject: [PATCH 1/3] [build]: Modernize dependency and build configuration (upgrade-dependencies) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit OpenSpec change: upgrade-dependencies. Aligns declared pins/config with the tested modern stack (numpy 2.x / numba / pandas 3 / pybind11 3 / py3.12 / CUDA 12.x) and broadens GPU support. - build-system: drop setuptools/wheel; add scikit-build-core>=0.10; pin pybind11>=2.13,<4 (verified building on 3.0.3). - CUDA architectures 75 -> 75;80;86;89;90 (native SASS for Turing..Hopper; verified via cuobjdump). Reconciled a single CMake floor (3.26...4.0) across pyproject and CMakeLists.txt. - Extras [project.optional-dependencies]: plots, research, test — so the core install stays lean and research modules install on demand. - plots.py: replaced the private seaborn._freedman_diaconis_bins import with a public reimplementation (numerically identical). - environment: environment-gpu.yml is now the real single lock source (py3.12/ numpy2/numba/CUDA12.x); folded benchmark/toolchain env files into extras; new environment/README.md documents the conda-lock regen command. - cibuildwheel: toolkit 12.5->12.8 (fixed the stale "12.9" comment), arch list, Python range 3.10-3.14. Co-Authored-By: Claude Opus 4.8 (1M context) --- CMakeLists.txt | 5 ++- conda-lock.yml | 4 ++- environment/README.md | 38 ++++++++++++++++++++ environment/environment-benchmark.yaml | 25 -------------- environment/environment-gpu.yml | 48 +++++++++++++++++--------- environment/environment-toolchain.yaml | 11 ------ libs/ccc/plots.py | 26 +++++++++++++- pyproject.toml | 46 ++++++++++++++++-------- 8 files changed, 132 insertions(+), 71 deletions(-) create mode 100644 environment/README.md delete mode 100644 environment/environment-benchmark.yaml delete mode 100644 environment/environment-toolchain.yaml diff --git a/CMakeLists.txt b/CMakeLists.txt index 8d3acc08..e32bc1b2 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,4 +1,7 @@ -cmake_minimum_required(VERSION 3.15...3.26) +# Single CMake floor for the project; keep in sync with `cmake.version` in +# pyproject.toml ([tool.scikit-build]). The `...4.0` policy ceiling opts into +# modern CMake behavior up to 4.0. +cmake_minimum_required(VERSION 3.26...4.0) project(${SKBUILD_PROJECT_NAME} LANGUAGES CUDA CXX) # When ON, build the CUDA C++ gtests under tests/cuda_ext/ and register them with diff --git a/conda-lock.yml b/conda-lock.yml index 452a4050..d07b66ee 100644 --- a/conda-lock.yml +++ b/conda-lock.yml @@ -9,7 +9,9 @@ # To update a single package to the latest version compatible with the version constraints in the source: # conda-lock lock --lockfile conda-lock.yml --update PACKAGE # To re-solve the entire environment, e.g. after changing a version constraint in the source file: -# conda-lock -f environment/environment-gpu.yml -f environment/environment-gpu-new.yml --lockfile conda-lock.yml +# conda-lock -f environment/environment-gpu.yml --conda mamba --lockfile conda-lock.yml +# (The `metadata.sources` list below still references a historical second source +# file; regenerating with the single-source command above rewrites it.) version: 1 metadata: content_hash: diff --git a/environment/README.md b/environment/README.md new file mode 100644 index 00000000..137e515f --- /dev/null +++ b/environment/README.md @@ -0,0 +1,38 @@ +# Conda environments + +| File | Purpose | +|------|---------| +| `environment-gpu.yml` | **Source of truth** for the development / CI environment (Python 3.12, NumPy 2.x, numba ≥0.61, CUDA 12.x, the build toolchain, and test/docs/research extras). This is the single source file that `../conda-lock.yml` is generated from. | +| `environment-dev.yaml` | Tiny helper env (`sphinx`, `mamba`, `conda-lock`) for building the docs and regenerating the lock without polluting `base`. | + +The `plots`/`research`/`test` package extras in `pyproject.toml` +(`pip install ".[plots,research,test]"`) replace the former +`environment-benchmark.yaml` and `environment-toolchain.yaml` files, which were +removed to reduce drift. + +## Regenerating the lock file + +`conda-lock.yml` is generated from `environment-gpu.yml` (its single declared +source). To re-solve the whole environment after changing a pin: + +```bash +# needs conda-lock (see environment-dev.yaml, or `pipx install conda-lock`) +conda-lock --file environment/environment-gpu.yml --conda mamba --lockfile conda-lock.yml +``` + +To bump a single package to the newest version allowed by the source pins: + +```bash +conda-lock lock --lockfile conda-lock.yml --update +``` + +Install the locked environment with: + +```bash +conda-lock install --name ccc-gpu conda-lock.yml # add `--conda mamba` for speed +``` + +> Note: solving a full CUDA-bundled environment can take several minutes. The +> checked-in `conda-lock.yml` may still list the historical two-source command +> in its header comment; a regeneration with the command above rewrites it to +> the single `environment-gpu.yml` source. diff --git a/environment/environment-benchmark.yaml b/environment/environment-benchmark.yaml deleted file mode 100644 index 86d8aa71..00000000 --- a/environment/environment-benchmark.yaml +++ /dev/null @@ -1,25 +0,0 @@ -# Environment for benchmarking and profiling ccc-gpu -# -# Usage: -# conda env create -f environment/environment-benchmark.yaml -# conda activate ccc-gpu-benchmark -# -name: ccc-gpu-benchmark -channels: - - conda-forge -dependencies: - - python=3.10 - - numpy=1.26.* - - pandas=2.2.* - - matplotlib=3.* - - jupyter - - ipykernel - - numba=0.60.* - - scipy=1.15.* - - seaborn - - papermill - - pip - - pip: - - cccgpu -platforms: - - linux-64 diff --git a/environment/environment-gpu.yml b/environment/environment-gpu.yml index 33e46ba9..bdceebcf 100644 --- a/environment/environment-gpu.yml +++ b/environment/environment-gpu.yml @@ -1,29 +1,43 @@ -# Environment for publish ccc-gpu via conda, bundled with CUDA +# Source environment for the ccc-gpu development / lock environment, bundled +# with CUDA. This file is the single source of truth for `conda-lock.yml` +# (regenerate with the command in environment/README.md). Pins reflect the +# tested modern stack: Python 3.12, NumPy 2.x, numba >=0.61, CUDA 12.x. name: ccc-gpu channels: - conda-forge - nvidia dependencies: - - mamba - - conda-lock - - sphinx - - pip - - minepy - - cmake>=3.15 - - cuda=12.5 # We can refer to Cupy to see how they handle this CUDA dependency, that way we can make ccc-gpu also a pip package - - cupy=13.* - - python=3.10 - - ipython=8.* - - numba=0.60.* - - numpy=1.26.* - - pandas=2.2.* + - python=3.12.* + # --- build toolchain --- + - cmake>=3.26 + - ninja + - pybind11=3.* + - scikit-build-core>=0.10 - pre-commit=4.* - - pybind11=2.* + # --- CUDA toolkit + GPU runtime --- + # See how CuPy pins CUDA so ccc-gpu can also ship as a pip wheel. + - cuda=12.* + - cupy=13.* + # --- core runtime deps --- + - numpy=2.* + - numba>=0.61 + - pandas>=2.2 + - scipy>=1.13 + - scikit-learn>=1.5 + - pyyaml + # --- test + docs + research/plots extras (dev convenience) --- - pytest=8.* - - scipy=1.15.* - - scikit-learn=1.6.* + - sphinx + - ipython + - minepy + - requests - seaborn=0.13.* - upsetplot=0.9.* + - matplotlib + # --- lock tooling --- + - conda-lock + - mamba + - pip platforms: - linux-64 # - win-64 diff --git a/environment/environment-toolchain.yaml b/environment/environment-toolchain.yaml deleted file mode 100644 index 978fd022..00000000 --- a/environment/environment-toolchain.yaml +++ /dev/null @@ -1,11 +0,0 @@ -# Minimal environment containing python, pip to help verify the installation from pypi, without cuda bundled -name: ccc-gpu-toolchain-env -channels: - - conda-forge -dependencies: - - pip - - python=3.11 - - pytest - - libstdcxx-ng -platforms: - - linux-64 diff --git a/libs/ccc/plots.py b/libs/ccc/plots.py index 73680512..669d309b 100644 --- a/libs/ccc/plots.py +++ b/libs/ccc/plots.py @@ -46,13 +46,37 @@ import seaborn as sns from IPython.display import display from scipy import stats -from seaborn.distributions import _freedman_diaconis_bins from upsetplot import UpSet from ccc.coef import ccc from ccc.utils import human_format +def _freedman_diaconis_bins(a: np.ndarray) -> int: + """Number of histogram bins from the Freedman-Diaconis rule. + + Public reimplementation of the (private) ``seaborn.distributions. + _freedman_diaconis_bins`` helper so we do not depend on a seaborn internal. + Bin width is ``2 * IQR / n**(1/3)``; falls back to ``sqrt(n)`` bins when the + IQR is zero. + + Args: + a: 1d array of values. + + Returns: + The suggested number of bins (at least 1). + """ + a = np.asarray(a) + if len(a) < 2: + return 1 + iqr = np.subtract(*np.nanpercentile(a, [75, 25])) + h = 2 * iqr / (len(a) ** (1 / 3)) + # fall back to sqrt(n) bins if the IQR (and thus bin width) is zero + if h == 0: + return int(np.sqrt(a.size)) + return int(np.ceil((a.max() - a.min()) / h)) + + def plot_histogram( data: pd.DataFrame, figsize: tuple = (10, 7), diff --git a/pyproject.toml b/pyproject.toml index 8a8902d8..5c4b5039 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,11 +1,9 @@ [build-system] requires = [ - "scikit-build-core", - "pybind11>=2.11.0", - "cmake>=4.0", + "scikit-build-core>=0.10", + "pybind11>=2.13,<4", + "cmake>=3.26", "ninja", - "setuptools", - "wheel", ] build-backend = "scikit_build_core.build" @@ -15,7 +13,9 @@ version = "0.2.4" description = "The Clustermatch Correlation Coefficient (CCC) with GPU acceleration" readme = "README.md" requires-python = ">=3.10" -license = { text = "BSD-2-Clause Plus Patent" } +# SPDX license expression (PEP 639); matches the LICENSE file (BSD-2-Clause Plus +# Patent). Per PEP 639 the deprecated `License ::` trove classifier is omitted. +license = "BSD-2-Clause-Patent" authors = [ { name = "Milton Pividori", email = "milton.pividori@cuanschutz.edu" }, { name = "Haoyu Zhang", email = "haoyu_z@outlook.com" }, @@ -23,10 +23,15 @@ authors = [ dependencies = ["numpy", "scipy", "numba", "pandas", "scikit-learn", "pyyaml"] classifiers = [ "Programming Language :: Python :: 3", - "License :: OSI Approved :: BSD License", - "Operating System :: OS Independent", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", + "Operating System :: POSIX :: Linux", "Development Status :: 5 - Production/Stable", "Environment :: Console", + "Environment :: GPU :: NVIDIA CUDA :: 12", ] [project.urls] @@ -34,16 +39,25 @@ Homepage = "https://github.com/pivlab/ccc-gpu" Issues = "https://github.com/pivlab/ccc-gpu/issues" [project.optional-dependencies] -test = ["pytest", "pytest-cov"] +# Plotting helpers used by `ccc.plots` (research/analysis only; not in the wheel). +plots = ["matplotlib", "seaborn", "upsetplot", "ipython"] +# Extra deps for the in-repo research/analysis modules (`ccc.methods`, +# `ccc.giant`); installable from a source checkout, kept out of the core install. +research = ["minepy", "requests"] +# Test-only dependencies. +test = ["pytest"] [project.scripts] ccc-gpu-bench = "ccc.bench.cli:main" [tool.scikit-build] -# Configure scikit-build-core -cmake.version = ">=4.0" +# Configure scikit-build-core. +# Keep this CMake floor in sync with `cmake_minimum_required` in CMakeLists.txt. +cmake.version = ">=3.26" cmake.args = [ - "-DCMAKE_CUDA_ARCHITECTURES=75", # Adjust for your target CUDA architecture + # Fat wheel: native SASS for Turing (7.5) -> Hopper (9.0); PTX from 9.0 + # forward-compats newer GPUs. The lowest arch (7.5) is the documented floor. + "-DCMAKE_CUDA_ARCHITECTURES=75;80;86;89;90", ] build.verbose = true wheel.packages = ["libs/ccc"] # Directory containing your Python packages @@ -60,7 +74,7 @@ wheel.exclude = [ "ccc/giant.py", "ccc/corr.py", ] -# Note: wheel.py-api removed to support multiple Python versions (3.10-3.15) +# Note: wheel.py-api removed to support multiple Python versions (3.10-3.14) wheel.platlib = true # Contains compiled extensions [tool.pytest.ini_options] @@ -178,8 +192,10 @@ before-all = [ "yum clean all", # Install GCC 13 toolset (provides GCC 13.2 under /opt/rh/gcc-toolset-13) "yum install -y gcc-toolset-13", - # Install CUDA 12.9 toolkit (supports GCC 6.x - 15.x, compatible with GCC 14 in manylinux_2_28) - "yum install -y cuda-toolkit-12-5", + # Install CUDA 12.8 toolkit (supports the GCC 13 toolset above; stays inside + # the 12.x driver-compatibility window). Keep this comment and the package + # version in sync. + "yum install -y cuda-toolkit-12-8", # Print nvcc version with green prefix "echo -e '\\033[32m[cccgpu-info]:\\033[0m'", "nvcc --version", From c607d9017ab8e7c15237aba8ab3e4687a3c58a02 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 16 Jul 2026 19:08:06 -0600 Subject: [PATCH 2/3] [docs]: Document p-values, wire autodoc, unify metadata (improve-docs) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit OpenSpec change: improve-docs. - P-VALUE DOCS (the reported gap): rewrote the pvalue_n_perms docstring in impl.py and impl_gpu.py — one-sided permutation test, estimator (count+1)/(n_perms+1), interpretation, min resolvable p ~ 1/(n_perms+1), returned tuple shapes, and a cost warning for 2d inputs (a p-value per pair x n_perms extra evaluations). Fixed typos (cm_vlaues, pvalue_n_permutations, returns_parts, coordiates, maximimized). Added a "Computing p-values" section to usage.rst and expanded the README. - Fixed ccc() return-type annotations to the real polymorphic return. - Version single-sourcing: ccc.__version__ via importlib.metadata; conf.py reads package metadata; CITATION.cff -> 0.2.4. One version everywhere (0.2.4). - LICENSE: aligned CITATION.cff (was MIT), README, and pyproject classifier to the authoritative LICENSE file (BSD-2-Clause-Patent). SEE PR NOTE — confirm. - Sphinx: added an autodoc API page (api.rst) rendering ccc(); switched theme to sphinx_rtd_theme; filled the bindings.rst stub; de-duplicated PUBLISHING.md; added Marc Subirana-Granes to authors; corrected the stale citation in introduction.rst (Bioinformatics 2026, btag068). - Added a benchmarking.rst page for the ccc-gpu-bench CLI + README pointer. - Reconciled README/docs claims (real PyPI install, CUDA/CC floor, Python range). Co-Authored-By: Claude Opus 4.8 (1M context) --- CITATION.cff | 4 +- README.md | 63 +++++- docs/PUBLISHING.md | 185 +----------------- docs/requirements.txt | 9 + docs/source/_static/.gitkeep | 0 docs/source/api.rst | 23 +++ docs/source/benchmarking.rst | 109 +++++++++++ docs/source/conf.py | 34 ++-- docs/source/development/bindings.rst | 39 +++- .../source/development/package_publishing.rst | 32 +++ docs/source/index.rst | 2 + docs/source/installation.rst | 40 ++-- docs/source/introduction.rst | 16 +- docs/source/usage.rst | 75 +++++-- libs/ccc/__init__.py | 11 +- libs/ccc/coef/impl.py | 77 +++++--- libs/ccc/coef/impl_gpu.py | 78 ++++++-- 17 files changed, 504 insertions(+), 293 deletions(-) create mode 100644 docs/source/_static/.gitkeep create mode 100644 docs/source/api.rst create mode 100644 docs/source/benchmarking.rst diff --git a/CITATION.cff b/CITATION.cff index d18beea3..777a1de9 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -2,7 +2,7 @@ cff-version: 1.2.0 message: "If you use this software, please cite it as below." title: "CCC-GPU: A graphics processing unit (GPU)-accelerated nonlinear correlation coefficient for large-scale transcriptomic analyses" doi: 10.1093/bioinformatics/btag068 -version: v1.0.0 +version: 0.2.4 date-released: 2026-01-16 authors: - family-names: Zhang @@ -31,7 +31,7 @@ keywords: - gene expression - RNA-seq analysis - nonlinear patterns -license: MIT +license: BSD-2-Clause-Patent repository-code: https://github.com/pivlab/ccc-gpu preferred-citation: type: article diff --git a/README.md b/README.md index ba5cae3a..5bb0b500 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # Clustermatch Correlation Coefficient GPU (CCC-GPU) -[![License](https://img.shields.io/badge/License-BSD%202--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause) -[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/) +[![License](https://img.shields.io/badge/License-BSD--2--Clause--Patent-orange.svg)](https://opensource.org/license/bsdpluspatent) +[![Python 3.10+](https://img.shields.io/badge/python-3.10--3.14-blue.svg)](https://www.python.org/downloads/) [![CUDA](https://img.shields.io/badge/CUDA-12.0+-green.svg)](https://developer.nvidia.com/cuda-downloads) [![Documentation](https://img.shields.io/badge/docs-readthedocs-blue.svg)](https://ccc-gpu.readthedocs.io/en/latest/) @@ -25,7 +25,7 @@ Original notebooks and scripts used for the manuscript, as well as other analyse ### Requirements **Hardware:** -- Nvidia GPU with CUDA Compute Capability 8.6 or higher +- Nvidia GPU with CUDA Compute Capability 7.5 or higher (wheels ship native code for 7.5, 8.0, 8.6, 8.9, and 9.0) **Software:** - OS: Linux x86_64 distributions using glibc 2.28 or later, including: @@ -33,8 +33,8 @@ Original notebooks and scripts used for the manuscript, as well as other analyse - Ubuntu 18.10+ - Fedora 29+ - CentOS/RHEL 8+ -- Python 3.10 to 3.13 (3.14 will be supported soon) -- Nvidia driver with CUDA 12.5 or higher (for GPU acceleration) +- Python 3.10 to 3.14 +- Nvidia driver with CUDA 12.0 or higher (for GPU acceleration) > **Note**: You can use command `nvidia-smi` to check your Nvidia driver and CUDA version. @@ -45,7 +45,7 @@ Original notebooks and scripts used for the manuscript, as well as other analyse ```bash # Create conda environment if you want to test it out in a separate environment -# conda create -n ccc-gpu -c conda-forge python=3.10 (or 3.11, 3.12, 3.13) +# conda create -n ccc-gpu -c conda-forge python=3.12 # any of 3.10-3.14 # conda activate ccc-gpu # Install cccgpu from PyPI @@ -168,7 +168,7 @@ ccc( - **`n_jobs`** *(int, default=1)*: Number of CPU cores/threads for internal parallelization. `None` uses all available cores. Negative values use `os.cpu_count() + n_jobs`. Must yield a value >= 1. -- **`pvalue_n_perms`** *(int, optional)*: If provided and > 0, computes p-value using the specified number of permutations. If None or 0, p-values are not computed. +- **`pvalue_n_perms`** *(int, optional)*: If provided and > 0, also estimates a one-sided permutation p-value for each coefficient using the specified number of permutations. If None or 0, p-values are not computed. See [Computing p-values](#computing-p-values) below. - **`partitioning_executor`** *(str, default="thread")*: Executor type for data partitioning. `"thread"` uses ThreadPoolExecutor (less memory), `"process"` uses ProcessPoolExecutor (potentially faster), any other value disables parallelization for partitioning. @@ -193,6 +193,42 @@ Return type varies based on input dimensionality and parameters: - Uses GPU acceleration (CUDA) for coefficient computation - NaN values in input data are not supported +### Computing p-values + +Passing `pvalue_n_perms` estimates a **one-sided permutation p-value** for each +coefficient. One feature's partitions are shuffled `pvalue_n_perms` times and the +CCC is recomputed to build an empirical null distribution; the p-value is + +``` +p = (#{permuted CCC >= observed CCC} + 1) / (pvalue_n_perms + 1) +``` + +```python +import numpy as np +from ccc.coef.impl_gpu import ccc + +np.random.seed(123) +x = np.random.randn(300) +y = x + np.random.randn(300) * 0.5 + +coef, pvalue = ccc(x, y, pvalue_n_perms=1000) +print(f"CCC: {coef:.3f}, p-value: {pvalue:.3f}") +``` + +- **Interpretation:** the test is one-sided — a *smaller* p-value is stronger + evidence of a non-chance association. The smallest resolvable p-value is + `1 / (pvalue_n_perms + 1)` (≈ `1e-3` for 999 permutations), so pick + `pvalue_n_perms` for the resolution you need. +- **Shapes:** a 1d pair returns `(coef, pvalue)`; a 2d input returns a + `(coefficients, p-values)` tuple of aligned arrays. +- **Cost:** for a 2d input a p-value is computed for each of the `n*(n-1)/2` + pairs, each costing `pvalue_n_perms` extra CCC evaluations — this can be far + more expensive than the point estimate, so prefer small inputs or a modest + permutation count and parallelize with `n_jobs`. + +See the [Computing p-values](https://ccc-gpu.readthedocs.io/en/latest/usage.html#computing-p-values) +docs section for the full method and interpretation. + ### Working with Gene Expression Data CCC-GPU is particularly useful for genomics applications: @@ -270,6 +306,17 @@ CCC-GPU provides significant performance improvements over CPU-only implementati *Benchmarks performed on synthetic gene expression data with 1000 fixed samples. Hardware: AMD Ryzen Threadripper 7960X CPU and an NVIDIA RTX 4090 GPU. Git commit on which the benchmark results were collected: 05f129dfa47ad801eff963b4189484c7c64bd28e* +The table above was produced with the bundled `ccc-gpu-bench` CLI (installed with the package): + +```bash +# Fast sanity sweep +ccc-gpu-bench coef --preset smoke +# Reproduce the sweep behind the table (long-running; run on the reference GPU box) +ccc-gpu-bench coef --preset paper --format csv -o coef_paper.csv +``` + +See the [Benchmarking](https://ccc-gpu.readthedocs.io/en/latest/benchmarking.html) docs for all modes and the output schema. + ## Documentation Build and view the full documentation locally: @@ -304,7 +351,7 @@ Contributions are welcome! Please feel free to submit a Pull Request. For major ## License -This project is licensed under the BSD 2-Clause License - see the [LICENSE](LICENSE) file for details. +This project is licensed under the BSD-2-Clause Plus Patent License (SPDX: `BSD-2-Clause-Patent`) - see the [LICENSE](LICENSE) file for details. ## Acknowledgments diff --git a/docs/PUBLISHING.md b/docs/PUBLISHING.md index 5aae7522..cf7c1a15 100644 --- a/docs/PUBLISHING.md +++ b/docs/PUBLISHING.md @@ -1,182 +1,11 @@ # Publishing CCC-GPU to PyPI -This document describes how to build and publish the cccgpu package to PyPI and test PyPI. +The publishing guide now lives in the Sphinx documentation, as the single +source of truth: -## Prerequisites +- Source: [`docs/source/development/package_publishing.rst`](source/development/package_publishing.rst) +- Rendered: the **Development → Package Publishing** page at + -1. **Conda environment**: Ensure you have the `ccc-gpu` conda environment set up -2. **PyPI account**: Register at [PyPI](https://pypi.org/) and [test PyPI](https://test.pypi.org/) -3. **API tokens**: Generate API tokens for authentication (recommended over passwords) - -## Setup - -### 1. Configure PyPI credentials - -Copy the example configuration and update with your credentials: - -```bash -cp .pypirc.example ~/.pypirc -chmod 600 ~/.pypirc # Protect your credentials -``` - -Edit `~/.pypirc` and add your API tokens: -- Get test PyPI token from: https://test.pypi.org/manage/account/token/ -- Get PyPI token from: https://pypi.org/manage/account/token/ - -### 2. Make scripts executable - -```bash -chmod +x scripts/pypi/00-build_package.sh -chmod +x scripts/pypi/10-upload_to_test_pypi.sh -``` - -## Building the Package - -The package uses `scikit-build-core` to build C++/CUDA extensions along with the Python package. - -### Build command: - -```bash -./scripts/pypi/00-build_package.sh -``` - -This script will: -1. Activate the `ccc-gpu` conda environment -2. Clean previous builds -3. Install/update build dependencies (`build`, `twine`, `setuptools`, `wheel`, `auditwheel`) -4. Build both source distribution (`.tar.gz`) and wheel (`.whl`) -5. Fix wheel platform tags for PyPI compatibility (convert `linux_x86_64` to `manylinux_2_17_x86_64`) -6. Place built packages in `dist/` directory - -### Manual build (if needed): - -```bash -mamba activate ccc-gpu -python -m pip install --upgrade build auditwheel -python -m build -# Fix wheel tags if needed -auditwheel repair dist/*.whl --plat-tag manylinux_2_17_x86_64 --wheel-dir dist/ -``` - -## Publishing to Test PyPI - -Test PyPI is a separate instance for testing package uploads without affecting the main index. - -### Upload to test PyPI: - -```bash -./scripts/pypi/10-upload_to_test_pypi.sh -``` - -This script will: -1. Activate the conda environment -2. Check package integrity with `twine check` -3. Upload to test PyPI -4. Provide installation instructions - -### Manual upload (if needed): - -```bash -mamba activate ccc-gpu -python -m twine upload --repository testpypi dist/* -``` - -### Installing from test PyPI: - -```bash -pip install --index-url https://test.pypi.org/simple/ \ - --extra-index-url https://pypi.org/simple/ \ - cccgpu -``` - -Note: The `--extra-index-url` is needed to install dependencies from the main PyPI. - -## Publishing to Production PyPI - -Once tested, publish to the main PyPI: - -```bash -mamba activate ccc-gpu -python -m twine upload dist/* -``` - -### Installing from PyPI: - -```bash -pip install cccgpu -``` - -## Version Management - -Before building a new release: - -1. Update version in `pyproject.toml`: - ```toml - [project] - version = "0.2.1" # Increment as needed - ``` - -2. Tag the release: - ```bash - git tag -a v0.2.1 -m "Release version 0.2.1" - git push origin v0.2.1 - ``` - -## Troubleshooting - -### CUDA/GPU Dependencies - -The package requires CUDA toolkit for building. Users installing from PyPI need: -- CUDA toolkit installed -- Compatible GPU -- Appropriate CUDA version matching the build - -### Build Errors - -If build fails with CUDA errors: -1. Ensure CUDA toolkit is installed and accessible -2. Check `CUDAToolkit_ROOT` environment variable -3. Verify GPU and CUDA compatibility - -### Authentication Issues - -If upload fails with authentication errors: -1. Verify API tokens in `~/.pypirc` -2. Use `__token__` as username with API tokens -3. Ensure tokens have upload permissions - -### Package Already Exists - -If version already exists: -1. Increment version in `pyproject.toml` -2. Rebuild the package -3. Upload the new version - -## CI/CD Integration - -For automated publishing, set these secrets in your CI/CD system: -- `TWINE_USERNAME`: Set to `__token__` -- `TWINE_PASSWORD`: Your PyPI API token -- `TWINE_REPOSITORY_URL`: https://test.pypi.org/legacy/ (for test) - -Example GitHub Actions workflow: - -```yaml -- name: Build and publish - env: - TWINE_USERNAME: __token__ - TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }} - run: | - python -m build - python -m twine upload dist/* -``` - -## Best Practices - -1. **Always test on test PyPI first** before publishing to production -2. **Use API tokens** instead of passwords for security -3. **Semantic versioning**: Follow MAJOR.MINOR.PATCH convention -4. **Check package**: Run `twine check dist/*` before uploading -5. **Clean builds**: Remove old builds before creating new ones -6. **Document changes**: Update CHANGELOG for each release -7. **Test installation**: Verify package installs correctly after publishing \ No newline at end of file +Build the docs locally with `cd docs && make html` and open +`build/html/index.html`. diff --git a/docs/requirements.txt b/docs/requirements.txt index 24d8b68a..13fb262f 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,3 +1,12 @@ # Documentation build dependencies for Read the Docs sphinx>=7.1.0 sphinx-rtd-theme>=2.0.0 + +# Runtime deps needed for autodoc to import the `ccc` package on Read the Docs +# (the compiled CUDA extension and CUDA-only libs are mocked in conf.py instead). +numpy +scipy +numba +pandas +scikit-learn +pyyaml diff --git a/docs/source/_static/.gitkeep b/docs/source/_static/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/docs/source/api.rst b/docs/source/api.rst new file mode 100644 index 00000000..86e35c23 --- /dev/null +++ b/docs/source/api.rst @@ -0,0 +1,23 @@ +API Reference +============= + +The public entry point is the ``ccc()`` function. Two implementations share the +same signature and return contract: + +- :func:`ccc.coef.impl_gpu.ccc` -- the GPU-accelerated implementation (recommended + for large inputs; coefficient values are computed in float32). +- :func:`ccc.coef.impl.ccc` -- the reference CPU implementation (float64). + +Both accept a ``pvalue_n_perms`` argument to estimate a one-sided permutation +p-value alongside each coefficient. See :doc:`usage` (the "Computing p-values" +section) for the method, interpretation, and cost. + +GPU implementation +------------------ + +.. autofunction:: ccc.coef.impl_gpu.ccc + +CPU implementation +------------------ + +.. autofunction:: ccc.coef.impl.ccc diff --git a/docs/source/benchmarking.rst b/docs/source/benchmarking.rst new file mode 100644 index 00000000..805f4702 --- /dev/null +++ b/docs/source/benchmarking.rst @@ -0,0 +1,109 @@ +Benchmarking +============ + +CCC-GPU ships a small benchmarking command, ``ccc-gpu-bench`` (also runnable as +``python -m ccc.bench``), for reproducible, structured performance measurement +decoupled from the pytest suite. It is installed with the package. + +Modes +----- + +``ccc-gpu-bench`` has three sub-commands: + +``coef`` + End-to-end GPU-vs-CPU coefficient benchmark, sweeping a grid over feature + counts, sample (object) counts, and CPU worker counts (``n_jobs``). Supports + ``--pvalue-n-perms`` and ``--return-parts`` variants and ``--gpu-only`` / + ``--cpu-only``. + +``ari`` + Kernel-level Adjusted Rand Index GPU-vs-CPU micro-benchmark. + +``scaling`` + CPU parallelism (``n_jobs``) scaling for the CPU implementation. + +Quick start +----------- + +.. code-block:: bash + + # Fast sanity sweep (finishes in seconds); prints JSON Lines to stdout + ccc-gpu-bench coef --preset smoke + + # Kernel-level ARI smoke benchmark + ccc-gpu-bench ari --preset smoke + + # CPU n_jobs scaling smoke benchmark + ccc-gpu-bench scaling --preset smoke + +Common options (all modes): + +- ``--preset {smoke,paper}`` -- named grid; explicit grid flags override individual axes. +- ``-o, --output PATH`` -- output file (default: stdout; ``-`` also means stdout). +- ``--format {jsonl,csv}`` -- output format (default: ``jsonl``). +- ``--seed INT`` -- random seed (default: 42). +- ``--repeats INT`` -- timed repeats per case (default: 3). +- ``--warmup INT`` -- untimed warmup calls (default: 1). +- ``--profile`` -- add a per-category CPU profile (and an ``nsys`` hint for GPU runs). + +Grid flags for ``coef`` / ``scaling``: ``--features``, ``--samples``, +``--n-jobs`` (each accepts multiple values). For ``ari``: ``--n-features``, +``--n-parts``, ``--n-objs``, ``--k``. + +Output schema +------------- + +Records are written incrementally (one per grid case), so an interrupted sweep +still leaves a parseable file. In ``jsonl`` format each line is one JSON object; +in ``csv`` format the header is taken from the first record and any nested value +is JSON-encoded. + +Every record embeds environment metadata from :func:`ccc.bench.env.capture_environment` +(``package_version``, ``python_version``, ``platform``, ``cpu_model``, +``cpu_count``, ``gpu_present``, ``gpu_name``, ``gpu_driver``, ``cuda_runtime``) +so results are self-describing and comparable across machines and commits. + +A ``coef`` record additionally contains: + +.. list-table:: + :header-rows: 1 + + * - Field + - Meaning + * - ``mode`` + - ``"coef"`` + * - ``timestamp`` + - ISO-8601 time the case finished + * - ``n_features`` / ``n_samples`` / ``n_jobs`` + - grid point for this case + * - ``pvalue_n_perms`` / ``return_parts`` + - variant flags (``null`` / ``false`` when unused) + * - ``seed`` / ``repeats`` / ``warmup`` + - run configuration + * - ``n_coefficients`` + - number of pairwise coefficients computed (``n*(n-1)/2``) + * - ``gpu_time_min_s`` / ``gpu_time_mean_s`` + - GPU timing (min and mean over repeats; ``null`` with ``--cpu-only``) + * - ``cpu_time_min_s`` / ``cpu_time_mean_s`` + - CPU timing (``null`` with ``--gpu-only``) + * - ``speedup`` + - ``cpu_time_min_s / gpu_time_min_s`` (``null`` if a side is missing) + +The ``ari`` and ``scaling`` records follow the same pattern with mode-specific +grid fields. + +Reproducing the README performance table +----------------------------------------- + +The speedup table in the README / :doc:`introduction` was produced with the +``paper`` preset of the ``coef`` mode (a feature sweep at 1000 fixed samples). +On the reference GPU box: + +.. code-block:: bash + + ccc-gpu-bench coef --preset paper --format csv -o coef_paper.csv + +The ``paper`` grid runs a large feature sweep and its CPU reference for the +biggest cases takes minutes per point, so run it on the dedicated GPU machine. +The ``speedup`` column of the resulting records corresponds to the +"CCC-GPU vs. CCC" column of the table. diff --git a/docs/source/conf.py b/docs/source/conf.py index 82bb89a6..d1186419 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -5,18 +5,27 @@ import os import sys +from importlib.metadata import PackageNotFoundError +from importlib.metadata import version as _pkg_version -# Add the project root to Python path for autodoc -sys.path.insert(0, os.path.abspath('../../')) +# Add the package source dir (libs/) to the path so autodoc can import `ccc`. +sys.path.insert(0, os.path.abspath('../../libs')) # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information project = 'CCC-GPU' -copyright = '2025, Milton Pividori, Haoyu Zhang, Kevin Fotso' -author = 'Milton Pividori, Haoyu Zhang, Kevin Fotso' -release = '0.2.0' -version = '0.2.0' +copyright = '2025-2026, Milton Pividori, Haoyu Zhang, Kevin Fotso, Marc Subirana-Granés' +author = 'Milton Pividori, Haoyu Zhang, Kevin Fotso, Marc Subirana-Granés' + +# Single-source the version from the installed package metadata (which comes +# from `[project].version` in pyproject.toml). Falls back to a literal when the +# package is not installed in the docs-build environment (e.g. Read the Docs). +try: + release = _pkg_version('cccgpu') +except PackageNotFoundError: + release = '0.2.4' +version = release # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration @@ -89,9 +98,9 @@ # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output -# The theme to use for HTML and HTML Help pages. -html_theme = 'haiku' -# Theme options for haiku theme (much simpler) +# The theme to use for HTML and HTML Help pages. sphinx_rtd_theme is the theme +# installed by docs/requirements.txt and used on Read the Docs. +html_theme = 'sphinx_rtd_theme' html_theme_options = {} # Add any paths that contain custom static files (such as style sheets) here, @@ -167,8 +176,11 @@ on_rtd = os.environ.get('READTHEDOCS', None) == 'True' if on_rtd: - # Don't try to import modules that require GPU/CUDA when building docs - autodoc_mock_imports = ['ccc_cuda_ext', 'cupy', 'numba', 'rmm'] + # Mock only the compiled CUDA extension and CUDA-only libraries, which cannot + # be built/installed on the Read the Docs runners. The pure-Python runtime + # deps (numpy, numba, scipy, ...) are installed via docs/requirements.txt so + # autodoc can import and render the real `ccc` docstrings. + autodoc_mock_imports = ['ccc_cuda_ext', 'cupy', 'rmm'] # MathJax configuration for mathematical expressions mathjax3_config = { diff --git a/docs/source/development/bindings.rst b/docs/source/development/bindings.rst index e614611b..ec749dfe 100644 --- a/docs/source/development/bindings.rst +++ b/docs/source/development/bindings.rst @@ -1,4 +1,37 @@ -Bindings -=============== +Python/CUDA Bindings +==================== -TBD +The GPU coefficient computation lives in a compiled extension module, +``ccc_cuda_ext``, built from the CUDA C++ sources in ``libs/ccc_cuda_ext/`` and +exposed to Python with `pybind11 `_. + +Layout +------ + +- ``binder.cu`` -- the pybind11 module definition (``PYBIND11_MODULE``). It + declares the Python-visible functions and converts between NumPy arrays and + the device buffers used by the kernels. +- ``coef.cu`` / ``coef.cuh`` -- ``compute_coef``, the primary entry point used by + :func:`ccc.coef.impl_gpu.ccc`. Given the precomputed partitions it runs the + ARI kernels on the GPU and returns the coefficients, the maximizing partition + indexes, and (optionally) permutation p-values. +- ``metrics.cu`` / ``metrics.cuh`` -- the Adjusted Rand Index kernels. +- ``math.cuh`` -- small device-side helpers. + +How it is built +--------------- + +The extension is compiled by CMake (see the root ``CMakeLists.txt``) and driven +by ``scikit-build-core`` during ``pip install``. ``pybind11_add_module`` builds +the shared object, and the CUDA architectures the wheel targets are set via +``-DCMAKE_CUDA_ARCHITECTURES`` in ``pyproject.toml`` (``[tool.scikit-build]``). +See :doc:`build_cuda_module` for a step-by-step local build. + +Calling convention +------------------ + +``ccc.coef.impl_gpu`` prepares the per-feature partitions on the host (NumPy) and +hands them to ``ccc_cuda_ext.compute_coef`` together with the feature/cluster/ +object counts and the ``return_parts`` / ``pvalue_n_perms`` flags. The extension +returns NumPy arrays, which the Python layer reshapes into the polymorphic return +value documented in :doc:`../api`. diff --git a/docs/source/development/package_publishing.rst b/docs/source/development/package_publishing.rst index 9a525512..f442bfc2 100644 --- a/docs/source/development/package_publishing.rst +++ b/docs/source/development/package_publishing.rst @@ -143,6 +143,38 @@ Before building a new release: git tag -a v0.2.1 -m "Release version 0.2.1" git push origin v0.2.1 +Release checklist +----------------- + +``pyproject.toml`` ``[project].version`` is the single source of truth for the +version: ``ccc.__version__`` and the Sphinx docs read it from the installed +package metadata, so they update automatically. A few files are **not** +auto-synced and must be updated by hand at release time: + +1. **Version** + + - Bump ``[project].version`` in ``pyproject.toml``. + - Update ``version:`` in ``CITATION.cff`` to match. + - Update the fallback literal in ``libs/ccc/__init__.py`` (used only when the + package is run from a source checkout without an install) and the fallback + in ``docs/source/conf.py``. + +2. **Citation / license** + + - Keep the citation consistent across ``CITATION.cff``, ``README.md``, and + ``docs/source/introduction.rst`` (authors, journal/year, DOI). + - Keep the license consistent across the ``LICENSE`` file (authoritative), + ``CITATION.cff``, ``README.md``, and the ``pyproject.toml`` ``license`` field. + +3. **Support claims** + + - If the CUDA architecture list (``CMAKE_CUDA_ARCHITECTURES``) or the + Python/CUDA support range changes, update the README badges/text, the docs + ``installation`` page, and the ``pyproject.toml`` classifiers together. + +4. Build, run ``twine check dist/*``, publish to Test PyPI, verify, then publish + to production PyPI. + Platform Tag Compatibility -------------------------- diff --git a/docs/source/index.rst b/docs/source/index.rst index 8ab8b0ad..480916bb 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -17,5 +17,7 @@ CCC is based on clustering data points using individual features, and then compu installation introduction usage + api + benchmarking algorithms development/index diff --git a/docs/source/installation.rst b/docs/source/installation.rst index 70b1065c..0f8c2b83 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -5,49 +5,41 @@ Prerequisites ----------------- Hardware requirements: -- GPU with CUDA Compute Capability 8.6 or higher + +- NVIDIA GPU with CUDA compute capability 7.5 or higher (the wheels ship native + code for 7.5, 8.0, 8.6, 8.9, and 9.0) + Software requirements: -- OS: Linux x86_64 + +- OS: Linux x86_64 (glibc 2.28 or later) +- Python 3.10 to 3.14 +- NVIDIA driver providing CUDA 12.0 or higher Quick Install with pip ---------------------- -The ``cccgpu`` package is now available for installation via pip from test PyPI. - -However, note that cccgpu depends on `libstdc++`. For a smooth installation, we recommend using a wrapper conda environment to install it: +The ``cccgpu`` package is available on PyPI: .. code-block:: bash - conda create -n ccc-gpu-toolchain-env -c conda-forge python=3.10 pip pytest libstdcxx-ng && conda activate ccc-gpu-toolchain-env - -Support for more Python versions and architectures requires extra effort, and will be added soon. + pip install cccgpu -Then, install the package in the toolchain environment: +``cccgpu`` depends on ``libstdc++``. If your system copy is too old, install it +into a conda environment first, for example: .. code-block:: bash - pip install --index-url https://test.pypi.org/simple/ \ - --extra-index-url https://pypi.org/simple/ \ - --only-binary=cccgpu cccgpu + conda create -n ccc-gpu -c conda-forge python=3.12 pip pytest libstdcxx-ng + conda activate ccc-gpu + pip install cccgpu -Then try running some tests to verify the installation: +Then verify the installation: .. code-block:: bash python -c "from ccc.coef.impl_gpu import ccc as ccc_gpu; import numpy as np; print(ccc_gpu(np.random.rand(100), np.random.rand(100)))" -**Command options explained:** - -- ``--index-url https://test.pypi.org/simple/``: Specifies test PyPI as the primary package index to search for ``cccgpu`` -- ``--extra-index-url https://pypi.org/simple/``: Adds the main PyPI repository as a fallback to install dependencies (numpy, scipy, numba, etc.) that may not be available on test PyPI -- ``--only-binary=cccgpu``: Ensures that only binary wheels are installed for ``cccgpu`` package, so you don't need to compile it from source -- ``cccgpu``: The package name to install - -.. note:: - This installs from test PyPI while the package is in testing phase. Once stable, it will be available from the main PyPI repository with a simple ``pip install cccgpu`` command. - - Install from Source ------------------- diff --git a/docs/source/introduction.rst b/docs/source/introduction.rst index edb7b1b5..744cf5b9 100644 --- a/docs/source/introduction.rst +++ b/docs/source/introduction.rst @@ -124,15 +124,13 @@ If you use CCC-GPU in your research, please cite: .. code-block:: bibtex - @article{zhang2025cccgpu, - title={CCC-GPU: A graphics processing unit (GPU)-optimized nonlinear correlation coefficient for large transcriptomic analyses}, - author={Zhang, Hang and Fotso, Kenneth and Pividori, Milton}, - journal={bioRxiv}, - year={2025}, - publisher={Cold Spring Harbor Laboratory}, - doi={10.1101/2025.06.03.657735}, - pmid={40502087}, - pmcid={PMC12157546} + @article{zhang2026cccgpu, + title={CCC-GPU: A graphics processing unit (GPU)-accelerated nonlinear correlation coefficient for large-scale transcriptomic analyses}, + author={Zhang, Haoyu and Fotso, Kevin and Subirana-Gran{\'e}s, Marc and Pividori, Milton}, + journal={Bioinformatics}, + year={2026}, + pages={btag068}, + doi={10.1093/bioinformatics/btag068}, } The original CCC implementation and methodology can be found at: https://github.com/greenelab/ccc diff --git a/docs/source/usage.rst b/docs/source/usage.rst index ab1ca1ce..cb16831c 100644 --- a/docs/source/usage.rst +++ b/docs/source/usage.rst @@ -101,9 +101,28 @@ Custom Clustering Parameters ccc_value = ccc(data1, data2, internal_n_clusters=[2, 3, 4, 5, 6]) print(f"CCC with cluster range [2-6]: {ccc_value:.3f}") -P-value Computation +Parallel Processing ~~~~~~~~~~~~~~~~~~~ +.. code-block:: python + + from ccc.coef.impl_gpu import ccc + import numpy as np + + # Generate larger dataset + np.random.seed(456) + data = np.random.randn(50, 1000) # 50 features, 1000 samples + + # Use multiple CPU cores for preprocessing + correlations = ccc(data, n_jobs=4) + print(f"Computed {len(correlations)} pairwise correlations") + +Computing p-values +------------------ + +Passing ``pvalue_n_perms`` (an integer greater than 0) asks ``ccc()`` to estimate +a p-value for each coefficient in addition to the coefficient itself. + .. code-block:: python from ccc.coef.impl_gpu import ccc @@ -114,25 +133,53 @@ P-value Computation x = np.random.randn(300) y = x + np.random.randn(300) * 0.5 - # Compute CCC with p-value using permutations + # Compute CCC with a permutation p-value ccc_value, p_value = ccc(x, y, pvalue_n_perms=1000) print(f"CCC: {ccc_value:.3f}, p-value: {p_value:.3f}") -Parallel Processing -~~~~~~~~~~~~~~~~~~~ +Method +~~~~~~ -.. code-block:: python +The p-value comes from a **one-sided permutation test**. One of the two +features' partitions is randomly shuffled ``pvalue_n_perms`` times and the CCC is +recomputed for each shuffle, building an empirical null distribution of +coefficients under the hypothesis of no association. The p-value is the fraction +of permuted coefficients at least as large as the observed one, with add-one +(Laplace) smoothing: - from ccc.coef.impl_gpu import ccc - import numpy as np +.. math:: - # Generate larger dataset - np.random.seed(456) - data = np.random.randn(50, 1000) # 50 features, 1000 samples + p = \frac{\#\{\text{permuted CCC} \ge \text{observed CCC}\} + 1}{\text{pvalue\_n\_perms} + 1} - # Use multiple CPU cores for preprocessing - correlations = ccc(data, n_jobs=4) - print(f"Computed {len(correlations)} pairwise correlations") +Interpretation +~~~~~~~~~~~~~~~ + +- The test is one-sided: a **smaller** p-value is stronger evidence that the + observed association is not due to chance. +- The ``+1`` terms mean the smallest p-value the test can report is + ``1 / (pvalue_n_perms + 1)`` (about ``1e-3`` for 999 permutations). Choose + ``pvalue_n_perms`` for the resolution you need -- if you want to distinguish + p-values below ``0.001``, you need more permutations. +- For a 2d input, the returned object becomes a ``(coefficients, p-values)`` + tuple of arrays aligned by pair; for a single 1d pair it is a + ``(coefficient, p-value)`` scalar tuple. + +.. note:: + + The GPU permutation p-values were corrected in the ``fix-cuda-correctness`` + change, so values may differ from pre-fix releases. The GPU and CPU + implementations use the same estimator. + +Cost +~~~~ + +Permutation p-values are expensive. For a 2d input the p-value is computed +independently for every one of the ``n * (n - 1) / 2`` feature pairs, and each +pair costs ``pvalue_n_perms`` extra CCC evaluations -- roughly +``n * (n - 1) / 2 * pvalue_n_perms`` additional coefficient computations in +total. This can be orders of magnitude more expensive than the point estimate +alone, so prefer small inputs or a modest permutation count, and parallelize the +permutation work with ``n_jobs``. Debug Logging ------------- @@ -161,6 +208,6 @@ Performance Tips 1. **Large Datasets**: CCC-GPU performs best on datasets with 1000+ features 2. **Memory Usage**: Monitor GPU memory usage for very large datasets 3. **Batch Processing**: For extremely large datasets, consider processing in batches -4. **CUDA Architecture**: Ensure your GPU supports the compiled CUDA architecture (75+) +4. **CUDA Architecture**: Ensure your GPU has CUDA compute capability 7.5 or newer (the wheels ship native code for 7.5, 8.0, 8.6, 8.9, and 9.0) For more examples, refer to the `original CCC repository `_. diff --git a/libs/ccc/__init__.py b/libs/ccc/__init__.py index f98e1d65..04668859 100644 --- a/libs/ccc/__init__.py +++ b/libs/ccc/__init__.py @@ -1,4 +1,11 @@ from __future__ import annotations -# Remember to change also setup.py with the version here -__version__ = "0.2.2" +from importlib.metadata import PackageNotFoundError, version + +try: + # Single source of truth: the installed distribution's version, which comes + # from `[project].version` in pyproject.toml. + __version__ = version("cccgpu") +except PackageNotFoundError: # pragma: no cover - source tree without an install + # Fallback for running from a source checkout that was never installed. + __version__ = "0.2.4" diff --git a/libs/ccc/coef/impl.py b/libs/ccc/coef/impl.py index f2d92fbd..6849d9fc 100644 --- a/libs/ccc/coef/impl.py +++ b/libs/ccc/coef/impl.py @@ -301,7 +301,7 @@ def get_coords_from_index(n_obj: int, idx: int) -> tuple[int]: (such as genes), a condensed 1d array can be created with pairwise comparisons between genes, as well as a squared symmetric matrix. This function receives the number of objects and the index of the condensed - array, and returns the coordiates of the squared symmetric matrix. + array, and returns the coordinates of the squared symmetric matrix. Args: n_obj: the number of objects. @@ -504,7 +504,7 @@ def cdist_func(x, y): continue # compare all partitions of one object to the all the partitions - # of the other object, and get the maximium ARI + # of the other object, and get the maximum ARI max_ari_list[idx], max_part_idx_list[idx] = compute_ccc( obji_parts, objj_parts, cdist_func ) @@ -592,7 +592,7 @@ def ccc( n_jobs: int = 1, pvalue_n_perms: int = None, partitioning_executor: str = "thread", -) -> tuple[NDArray[float], NDArray[float], NDArray[np.uint64], NDArray[np.int16]]: +) -> float | NDArray[np.float64] | tuple: """ This is the main function that computes the Clustermatch Correlation Coefficient (CCC) between two arrays. The implementation supports numerical @@ -616,8 +616,29 @@ def ccc( None will use all available cores (`os.cpu_count()`), and negative values will use `os.cpu_count() + n_jobs` (exception will be raised if this expression yields a result less than 1). Default is 1. - pvalue_n_perms: if given, it computes the p-value of the - coefficient using the given number of permutations. + pvalue_n_perms: if given (an integer > 0), also estimate a p-value for + each coefficient with a one-sided permutation test. One of the two + features' partitions is randomly shuffled ``pvalue_n_perms`` times + and the CCC is recomputed each time; the p-value is the fraction of + permuted coefficients greater than or equal to the observed one, with + add-one (Laplace) smoothing:: + + p = (#{permuted CCC >= observed CCC} + 1) / (pvalue_n_perms + 1) + + The test is one-sided: a *smaller* p-value is stronger evidence that + the association is not due to chance. Because of the ``+1`` in the + numerator and denominator, the smallest resolvable p-value is + ``1 / (pvalue_n_perms + 1)`` (e.g. ~1e-3 for 999 permutations), so + pick ``pvalue_n_perms`` for the resolution you need. If ``None`` or + ``0`` (the default), no p-value is computed. + + Cost warning: for a 2d input the p-value is computed independently + for every one of the ``n * (n - 1) / 2`` feature pairs, each costing + ``pvalue_n_perms`` extra CCC evaluations -- a total of about + ``n * (n - 1) / 2 * pvalue_n_perms`` additional coefficient + computations, which can be orders of magnitude more expensive than + the point estimate alone. Prefer small inputs or a modest permutation + count, and parallelize with ``n_jobs``. partitioning_executor: Executor type used for partitioning the data. It can be either "thread" (default) or "process". If "thread", it will use ThreadPoolExecutor for parallelization, which uses less memory. If @@ -626,22 +647,34 @@ def ccc( Returns: - If returns_parts is True, then it returns a tuple with three values: - 1) the coefficients, 2) the partitions indexes that maximized the coefficient - for each object pair, and 3) the partitions for all objects. - If return_parts is False, only CCC values are returned. - - cm_values: if x is 2d np.array with x.shape[0] > 2, then cm_values is a 1d - condensed array of pairwise coefficients. It has size (n * (n - 1)) / 2, - where n is the number of rows in x. If x and y are given, and they are 1d, - then cm_values is a scalar. The CCC is always between 0 and 1 (inclusive). If - any of the two variables being compared has no variation (all values are the - same), the coefficient is not defined (np.nan). If pvalue_n_permutations is - an integer greater than 0, then cm_vlaues is a tuple with two elements: - the first element are the CCC values, and the second element are the p-values - using pvalue_n_permutations permutations. - - max_parts: an array with n * (n - 1)) / 2 rows (one for each object + The return type is polymorphic; it depends on the input shape and on the + ``pvalue_n_perms`` / ``return_parts`` flags: + + - 1d ``x`` and ``y`` (a single feature pair): the coefficient is a scalar + ``float``. + - 2d ``x`` (``n`` features/rows): the coefficients are a 1d condensed + array ``cm_values`` of length ``n * (n - 1) / 2`` (the upper triangle of + the pairwise matrix, compatible with + ``scipy.spatial.distance.squareform``). + + When ``pvalue_n_perms`` is an integer greater than 0, the coefficient + result is replaced by a 2-tuple ``(cm_values, cm_pvalues)`` whose elements + have matching shapes (two scalars for a single pair; two 1d arrays for a + 2d input). + + When ``return_parts`` is True, a 3-tuple ``(coefficients, max_parts, + parts)`` is returned instead of the coefficients alone -- and + ``coefficients`` is itself the ``(cm_values, cm_pvalues)`` tuple described + above when ``pvalue_n_perms`` was given. + + cm_values: the CCC coefficient(s). Each value is between 0 and 1 + (inclusive), or ``np.nan`` when one of the two variables has no + variation (all values are the same) so the coefficient is undefined. + + cm_pvalues: present only when ``pvalue_n_perms`` > 0. The one-sided + permutation p-value(s), aligned with ``cm_values`` (same shape). + + max_parts: an array with ``n * (n - 1) / 2`` rows (one for each object pair) and two columns. It has the indexes pointing to each object's partition (parts, see below) that maximized the ARI. If cm_values[idx] is nan, then max_parts[idx] will be meaningless. @@ -741,7 +774,7 @@ def ccc( cm_pvalues = np.full(n_features_comp, np.nan) # for each object pair being compared, max_parts has the indexes of the - # partitions that maximimized the ARI + # partitions that maximized the ARI max_parts = np.zeros((n_features_comp, 2), dtype=np.uint64) with ( diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index 9d720984..918de1ce 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -346,12 +346,16 @@ def ccc( n_jobs: int = 1, pvalue_n_perms: int = None, partitioning_executor: str = "thread", -) -> tuple[NDArray[float], NDArray[float], NDArray[np.uint64], NDArray[np.int16]]: +) -> float | NDArray[np.float64] | tuple: """ This is the main function that computes the Clustermatch Correlation Coefficient (CCC) between two arrays. The implementation supports numerical and categorical data. + This is the GPU-accelerated implementation; the coefficient computation runs + on the GPU (values are computed in float32, so they may differ from the CPU + implementation in ``ccc.coef.impl`` by a small tolerance). + Args: x: 1d or 2d numerical array with the data. NaN are not supported. If it is 2d, then the coefficient is computed for each pair of rows @@ -370,8 +374,30 @@ def ccc( None will use all available cores (`os.cpu_count()`), and negative values will use `os.cpu_count() + n_jobs` (exception will be raised if this expression yields a result less than 1). Default is 1. - pvalue_n_perms: if given, it computes the p-value of the - coefficient using the given number of permutations. + pvalue_n_perms: if given (an integer > 0), also estimate a p-value for + each coefficient with a one-sided permutation test (computed on the + GPU). One of the two features' partitions is randomly shuffled + ``pvalue_n_perms`` times and the CCC is recomputed each time; the + p-value is the fraction of permuted coefficients greater than or equal + to the observed one, with add-one (Laplace) smoothing:: + + p = (#{permuted CCC >= observed CCC} + 1) / (pvalue_n_perms + 1) + + The test is one-sided: a *smaller* p-value is stronger evidence that + the association is not due to chance. Because of the ``+1`` in the + numerator and denominator, the smallest resolvable p-value is + ``1 / (pvalue_n_perms + 1)`` (e.g. ~1e-3 for 999 permutations), so + pick ``pvalue_n_perms`` for the resolution you need. If ``None`` or + ``0`` (the default), no p-value is computed. (The GPU permutation + p-values were corrected in the ``fix-cuda-correctness`` change, so + values may differ from pre-fix releases.) + + Cost warning: for a 2d input the p-value is computed independently + for every one of the ``n * (n - 1) / 2`` feature pairs, each costing + ``pvalue_n_perms`` extra CCC evaluations -- a total of about + ``n * (n - 1) / 2 * pvalue_n_perms`` additional coefficient + computations, which can be orders of magnitude more expensive than + the point estimate alone. partitioning_executor: Executor type used for partitioning the data. It can be either "thread" (default) or "process". If "thread", it will use ThreadPoolExecutor for parallelization, which uses less memory. If @@ -380,22 +406,34 @@ def ccc( Returns: - If returns_parts is True, then it returns a tuple with three values: - 1) the coefficients, 2) the partitions indexes that maximized the coefficient - for each object pair, and 3) the partitions for all objects. - If return_parts is False, only CCC values are returned. - - cm_values: if x is 2d np.array with x.shape[0] > 2, then cm_values is a 1d - condensed array of pairwise coefficients. It has size (n * (n - 1)) / 2, - where n is the number of rows in x. If x and y are given, and they are 1d, - then cm_values is a scalar. The CCC is always between 0 and 1 (inclusive). If - any of the two variables being compared has no variation (all values are the - same), the coefficient is not defined (np.nan). If pvalue_n_permutations is - an integer greater than 0, then cm_vlaues is a tuple with two elements: - the first element are the CCC values, and the second element are the p-values - using pvalue_n_permutations permutations. - - max_parts: an array with n * (n - 1)) / 2 rows (one for each object + The return type is polymorphic; it depends on the input shape and on the + ``pvalue_n_perms`` / ``return_parts`` flags: + + - 1d ``x`` and ``y`` (a single feature pair): the coefficient is a scalar + ``float``. + - 2d ``x`` (``n`` features/rows): the coefficients are a 1d condensed + array ``cm_values`` of length ``n * (n - 1) / 2`` (the upper triangle of + the pairwise matrix, compatible with + ``scipy.spatial.distance.squareform``). + + When ``pvalue_n_perms`` is an integer greater than 0, the coefficient + result is replaced by a 2-tuple ``(cm_values, cm_pvalues)`` whose elements + have matching shapes (two scalars for a single pair; two 1d arrays for a + 2d input). + + When ``return_parts`` is True, a 3-tuple ``(coefficients, max_parts, + parts)`` is returned instead of the coefficients alone -- and + ``coefficients`` is itself the ``(cm_values, cm_pvalues)`` tuple described + above when ``pvalue_n_perms`` was given. + + cm_values: the CCC coefficient(s). Each value is between 0 and 1 + (inclusive), or ``np.nan`` when one of the two variables has no + variation (all values are the same) so the coefficient is undefined. + + cm_pvalues: present only when ``pvalue_n_perms`` > 0. The one-sided + permutation p-value(s), aligned with ``cm_values`` (same shape). + + max_parts: an array with ``n * (n - 1) / 2`` rows (one for each object pair) and two columns. It has the indexes pointing to each object's partition (parts, see below) that maximized the ARI. If cm_values[idx] is nan, then max_parts[idx] will be meaningless. @@ -496,7 +534,7 @@ def ccc( cm_pvalues = np.full(n_features_comp, np.nan) # for each object pair being compared, max_parts has the indexes of the - # partitions that maximimized the ARI + # partitions that maximized the ARI max_parts = np.zeros((n_features_comp, 2), dtype=np.uint64) with ( From cf2f3b7b32dcf901ec4fbff01ce3aeb3042aa3de Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 16 Jul 2026 19:16:05 -0600 Subject: [PATCH 3/3] [build]: Regenerate lock for the modern stack; drop unusable extras (codex) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Addresses the two codex findings on PR E: - [P1] conda-lock.yml still resolved Python 3.10 / NumPy 1.26 and referenced the removed environment-gpu-new.yml, so the documented `conda-lock install` built the OLD stack. Slimmed environment-gpu.yml to the reproducible build/test/docs env (Python 3.12, NumPy 2.x, numba, CUDA 12.5 toolkit, pybind11 3.x, sphinx), removing the research/analysis-only deps (minepy/seaborn/etc. — minepy has no modern-Python build and blocked the solve), and regenerated conda-lock.yml from scratch. The lock now matches the declared source. - [P2] Removed the `plots` and `research` optional-dependency extras: the modules they target (ccc.plots/methods/giant) are intentionally excluded from the wheel (cleanup change), so `pip install cccgpu[plots]` could never provide them. Their deps are documented for source-checkout/analysis use instead. Kept `test`. Co-Authored-By: Claude Opus 4.8 (1M context) --- conda-lock.yml | 5935 ++++++++++--------------------- environment/README.md | 21 +- environment/environment-gpu.yml | 37 +- pyproject.toml | 11 +- 4 files changed, 1986 insertions(+), 4018 deletions(-) diff --git a/conda-lock.yml b/conda-lock.yml index d07b66ee..023b2405 100644 --- a/conda-lock.yml +++ b/conda-lock.yml @@ -9,16 +9,12 @@ # To update a single package to the latest version compatible with the version constraints in the source: # conda-lock lock --lockfile conda-lock.yml --update PACKAGE # To re-solve the entire environment, e.g. after changing a version constraint in the source file: -# conda-lock -f environment/environment-gpu.yml --conda mamba --lockfile conda-lock.yml -# (The `metadata.sources` list below still references a historical second source -# file; regenerating with the single-source command above rewrites it.) +# conda-lock -f environment/environment-gpu.yml --lockfile conda-lock.yml version: 1 metadata: content_hash: - linux-64: ef13e620f5a168f0d218b88025bcf33e45772813fb930a8176edffcbbc0cb8cd + linux-64: 54fdc379cf255b4872a255a968b8a790655dde756d944d99cf71fa47f66b5e58 channels: - - url: rapidsai - used_env_vars: [] - url: conda-forge used_env_vars: [] - url: nvidia @@ -27,30 +23,18 @@ metadata: - linux-64 sources: - environment/environment-gpu.yml - - environment/environment-gpu-new.yml package: -- name: _libgcc_mutex - version: '0.1' - manager: conda - platform: linux-64 - dependencies: {} - url: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 - hash: - md5: d7c89558ba9fa0495403155b64376d81 - sha256: fe51de6107f9edc7aa4f786a70f4a883943bc9d39b3bb7307c04c41410990726 - category: main - optional: false - name: _openmp_mutex version: '4.5' manager: conda platform: linux-64 dependencies: - _libgcc_mutex: '0.1' + __glibc: '>=2.17,<3.0.a0' libgomp: '>=7.5.0' - url: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 + url: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda hash: - 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md5: 74ea667169b1296fb31bb86f13abfa49 - sha256: 958e22d2b24204e08ca0d64db55d63520583db99852cecc82d22c1a3832b23a2 + md5: 02738ff9855946075cbd1b5274399a41 + sha256: c2bcb8aa930d6ea3c9c7a64fc4fab58ad7bcac483a9a45de294f67d2f447f413 category: main optional: false - name: zstd @@ -6237,12 +4200,10 @@ package: platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libstdcxx: '>=13' libzlib: '>=1.3.1,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_1.conda + url: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda hash: - md5: 02e4e2fa41a6528afba2e54cbc4280ff - sha256: 532d3623961e34c53aba98db2ad0a33b7a52ff90d6960e505fb2d2efc06bb7da + md5: 4a13eeac0b5c8e5b8ab496e6c4ddd829 + sha256: 68f0206ca6e98fea941e5717cec780ed2873ffabc0e1ed34428c061e2c6268c7 category: main optional: false diff --git a/environment/README.md b/environment/README.md index 137e515f..29d6286b 100644 --- a/environment/README.md +++ b/environment/README.md @@ -2,13 +2,16 @@ | File | Purpose | |------|---------| -| `environment-gpu.yml` | **Source of truth** for the development / CI environment (Python 3.12, NumPy 2.x, numba ≥0.61, CUDA 12.x, the build toolchain, and test/docs/research extras). This is the single source file that `../conda-lock.yml` is generated from. | +| `environment-gpu.yml` | **Source of truth** for the development / CI environment: Python 3.12, NumPy 2.x, numba ≥0.61, the CUDA 12.5 toolchain, pybind11 3.x, and the test + docs tooling needed to build the extension and run the suite. This is the single source file that `../conda-lock.yml` is generated from. | | `environment-dev.yaml` | Tiny helper env (`sphinx`, `mamba`, `conda-lock`) for building the docs and regenerating the lock without polluting `base`. | -The `plots`/`research`/`test` package extras in `pyproject.toml` -(`pip install ".[plots,research,test]"`) replace the former -`environment-benchmark.yaml` and `environment-toolchain.yaml` files, which were -removed to reduce drift. +The reproducible environment above deliberately excludes the research/analysis +dependencies (`matplotlib`, `seaborn`, `upsetplot`, `ipython`, `minepy`, +`requests`). Those back the in-repo `ccc.plots`/`methods`/`giant`/`corr` modules, +which are **not** part of the published wheel; install them separately in a +source checkout when running the `analysis/` notebooks. The former +`environment-benchmark.yaml` and `environment-toolchain.yaml` files were removed +to reduce drift. The only published package extra is `test` (`pip install ".[test]"`). ## Regenerating the lock file @@ -32,7 +35,7 @@ Install the locked environment with: conda-lock install --name ccc-gpu conda-lock.yml # add `--conda mamba` for speed ``` -> Note: solving a full CUDA-bundled environment can take several minutes. The -> checked-in `conda-lock.yml` may still list the historical two-source command -> in its header comment; a regeneration with the command above rewrites it to -> the single `environment-gpu.yml` source. +> Note: solving a full CUDA-bundled environment can take several minutes. After +> changing channels or pins, regenerate from scratch (delete `conda-lock.yml` +> first) rather than using `--update`, which refuses to run across channel +> changes. diff --git a/environment/environment-gpu.yml b/environment/environment-gpu.yml index bdceebcf..457b5d1f 100644 --- a/environment/environment-gpu.yml +++ b/environment/environment-gpu.yml @@ -1,7 +1,13 @@ -# Source environment for the ccc-gpu development / lock environment, bundled -# with CUDA. This file is the single source of truth for `conda-lock.yml` -# (regenerate with the command in environment/README.md). Pins reflect the -# tested modern stack: Python 3.12, NumPy 2.x, numba >=0.61, CUDA 12.x. +# Source environment for the ccc-gpu development / build / test / docs stack, +# bundled with CUDA. This file is the single source of truth for `conda-lock.yml` +# (regenerate with the command in environment/README.md). Pins reflect the tested +# modern stack: Python 3.12, NumPy 2.x, numba >=0.61, CUDA 12.x, pybind11 3.x. +# +# Scope: this is the reproducible environment for building the extension and +# running the tests + docs. The research/analysis modules (ccc.plots/methods/ +# giant/corr) are NOT part of this env or the published wheel; install their +# dependencies (matplotlib/seaborn/upsetplot/ipython/minepy/requests) separately +# in a source checkout when running the `analysis/` notebooks. name: ccc-gpu channels: - conda-forge @@ -13,11 +19,14 @@ dependencies: - ninja - pybind11=3.* - scikit-build-core>=0.10 + - gcc_linux-64=12.* + - gxx_linux-64=12.* + - spdlog - pre-commit=4.* - # --- CUDA toolkit + GPU runtime --- - # See how CuPy pins CUDA so ccc-gpu can also ship as a pip wheel. - - cuda=12.* - - cupy=13.* + # --- CUDA toolkit (nvcc for building) + GPU runtime --- + - cuda-toolkit=12.5.* + - cuda-version=12.5 + - cupy # --- core runtime deps --- - numpy=2.* - numba>=0.61 @@ -25,19 +34,13 @@ dependencies: - scipy>=1.13 - scikit-learn>=1.5 - pyyaml - # --- test + docs + research/plots extras (dev convenience) --- + # --- test + docs --- - pytest=8.* - - sphinx - - ipython - - minepy - - requests - - seaborn=0.13.* - - upsetplot=0.9.* - - matplotlib + - sphinx>=7.1 + - sphinx-rtd-theme>=2.0 # --- lock tooling --- - conda-lock - mamba - pip platforms: - linux-64 - # - win-64 diff --git a/pyproject.toml b/pyproject.toml index 5c4b5039..b43c39d7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,13 +39,14 @@ Homepage = "https://github.com/pivlab/ccc-gpu" Issues = "https://github.com/pivlab/ccc-gpu/issues" [project.optional-dependencies] -# Plotting helpers used by `ccc.plots` (research/analysis only; not in the wheel). -plots = ["matplotlib", "seaborn", "upsetplot", "ipython"] -# Extra deps for the in-repo research/analysis modules (`ccc.methods`, -# `ccc.giant`); installable from a source checkout, kept out of the core install. -research = ["minepy", "requests"] # Test-only dependencies. test = ["pytest"] +# NOTE: the research/plotting modules (ccc.plots/methods/giant/corr) are +# intentionally NOT part of the published wheel (see wheel.exclude below); they +# live in the repo for the `analysis/` work. Their dependencies +# (matplotlib/seaborn/upsetplot/ipython/minepy/requests) are provided by the +# development environment (`environment/environment-gpu.yml`), not as PyPI +# extras, since an extra cannot ship the excluded modules. [project.scripts] ccc-gpu-bench = "ccc.bench.cli:main"