diff --git a/.github/actions/build/action.yml b/.github/actions/build/action.yml
index 3011cc1..23e4667 100644
--- a/.github/actions/build/action.yml
+++ b/.github/actions/build/action.yml
@@ -4,43 +4,31 @@ description: Build the package
runs:
using: "composite"
steps:
- - uses: actions/checkout@v3
-
- - name: Set up Python
- uses: actions/setup-python@v3
+ - uses: actions/checkout@v4
+
+ - name: Install uv
+ uses: astral-sh/setup-uv@v5
with:
python-version: '3.14'
- - name: Install dependencies
- run: |
- python3 -m pip install --upgrade pip
- python3 -m pip install flake8 pytest
- python3 -m pip install poetry
- python3 -m pip install twine
- shell: bash
-
- - name: Lint with flake8
+ - name: Lint with ruff
run: |
# stop the build if there are Python syntax errors or undefined names
- flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
- # exit-zero treats all errors as warnings.
- flake8 . --count --exit-zero --max-complexity=10 --max-line-length=88 --statistics
+ uvx ruff check . --select=E9,F63,F7,F82 --output-format=full
+ # exit-zero treats all findings (line length + complexity) as warnings
+ uvx ruff check . --select=E501,C901 --line-length=88 --exit-zero --statistics
shell: bash
- - name: Install CVMatrix dependencies
- run: poetry install
- shell: bash
-
- name: Build a binary wheel and a source tarball
- run: poetry build
+ run: uv build
shell: bash
-
+
- name: Check the distribution with twine
- run: twine check dist/*
+ run: uvx twine check dist/*
shell: bash
-
+
- name: Store the distribution packages
uses: actions/upload-artifact@v4
with:
name: python-package-distributions
- path: dist/
\ No newline at end of file
+ path: dist/
diff --git a/.github/actions/test/action.yml b/.github/actions/test/action.yml
index ed89ad9..c675176 100644
--- a/.github/actions/test/action.yml
+++ b/.github/actions/test/action.yml
@@ -5,31 +5,33 @@ inputs:
PYTHON_VERSION:
description: Test Python version
required: true
+ INSTALL_JAX:
+ description: Whether to install the optional JAX backend extra (true/false)
+ required: false
+ default: 'true'
runs:
using: "composite"
steps:
- - name: Set up Python
- uses: actions/setup-python@v5
+ - name: Install uv
+ uses: astral-sh/setup-uv@v5
with:
python-version: ${{ inputs.PYTHON_VERSION }}
- - name: Install Poetry
- run: |
- python -m pip install --upgrade pip
- python -m pip install poetry
- shell: bash
-
- - name: Install project dependencies
- run: poetry install
+ - name: Install project dependencies (with JAX backend)
+ if: ${{ inputs.INSTALL_JAX == 'true' }}
+ run: uv sync --extra jax --group dev
shell: bash
- - name: Install additional dependencies for testing
- run: |
- poetry add --group dev pandas pytest flake8 pytest-cov typeguard
+ - name: Install project dependencies (numpy-only, without JAX)
+ if: ${{ inputs.INSTALL_JAX != 'true' }}
+ run: uv sync --group dev
shell: bash
+ # The same test suite runs in both modes. With the JAX backend installed it also
+ # exercises the parametrized backend="jax" tests; without JAX those parametrizations
+ # are skipped (pytest.importorskip), verifying the numpy-only path.
- name: Run tests
run: |
- poetry run pytest tests --doctest-modules --junitxml=junit/test-results.xml --cov=cvmatrix/ --cov-report=xml --typeguard-packages=cvmatrix/
+ uv run pytest tests --doctest-modules --junitxml=junit/test-results.xml --cov=cvmatrix/ --cov-report=xml --typeguard-packages=cvmatrix/
shell: bash
diff --git a/.github/workflows/pull_request_package_workflow.yml b/.github/workflows/pull_request_package_workflow.yml
index fad9e84..28f6cb1 100644
--- a/.github/workflows/pull_request_package_workflow.yml
+++ b/.github/workflows/pull_request_package_workflow.yml
@@ -8,7 +8,7 @@ on:
- 'tests/**'
- '.github/**'
- 'pyproject.toml'
- - 'poetry.lock'
+ - 'uv.lock'
jobs:
build_package:
diff --git a/.github/workflows/pull_request_test_workflow.yml b/.github/workflows/pull_request_test_workflow.yml
index 9e29b2b..0e35627 100644
--- a/.github/workflows/pull_request_test_workflow.yml
+++ b/.github/workflows/pull_request_test_workflow.yml
@@ -8,14 +8,15 @@ on:
- 'tests/**'
- '.github/**'
- 'pyproject.toml'
- - 'poetry.lock'
+ - 'uv.lock'
jobs:
- test_package:
+ # Full test suite with the JAX backend installed (also runs backend="jax" tests)
+ test_package_with_jax:
runs-on: ${{ matrix.os }}
- strategy:
- fail-fast: false
- matrix:
+ strategy:
+ fail-fast: false
+ matrix:
os: [ubuntu-latest, windows-latest, macos-latest]
python-version: ["3.11", "3.12", "3.13", "3.14"]
steps:
@@ -23,17 +24,41 @@ jobs:
- uses: ./.github/actions/test
with:
PYTHON_VERSION: ${{ matrix.python-version }}
-
+ INSTALL_JAX: 'true'
+
+ - name: Upload coverage to Coveralls
+ uses: coverallsapp/github-action@v2
+ with:
+ github-token: ${{ secrets.GITHUB_TOKEN }}
+ path-to-lcov: coverage.xml
+ flag-name: ${{ matrix.os }}-python-${{ matrix.python-version }}-jax
+ parallel: true
+
+ # Numpy-only installation (without JAX); backend="jax" tests are skipped
+ test_package_numpy_only:
+ runs-on: ${{ matrix.os }}
+ strategy:
+ fail-fast: false
+ matrix:
+ os: [ubuntu-latest, windows-latest, macos-latest]
+ python-version: ["3.11", "3.12", "3.13", "3.14"]
+ steps:
+ - uses: actions/checkout@v4
+ - uses: ./.github/actions/test
+ with:
+ PYTHON_VERSION: ${{ matrix.python-version }}
+ INSTALL_JAX: 'false'
+
- name: Upload coverage to Coveralls
uses: coverallsapp/github-action@v2
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
path-to-lcov: coverage.xml
- flag-name: ${{ matrix.os }}-python-${{ matrix.python-version }}
+ flag-name: ${{ matrix.os }}-python-${{ matrix.python-version }}-numpy-only
parallel: true
finish:
- needs: test_package
+ needs: [test_package_with_jax, test_package_numpy_only]
runs-on: ubuntu-latest
steps:
- name: Finish Coveralls
diff --git a/.github/workflows/test_workflow.yml b/.github/workflows/test_workflow.yml
index 0b90c33..3c01227 100644
--- a/.github/workflows/test_workflow.yml
+++ b/.github/workflows/test_workflow.yml
@@ -8,14 +8,15 @@ on:
- 'tests/**'
- '.github/**'
- 'pyproject.toml'
- - 'poetry.lock'
+ - 'uv.lock'
jobs:
- test_package:
- runs-on: ${{ matrix.os }}
- strategy:
- fail-fast: false
- matrix:
+ # Full test suite with the JAX backend installed (also runs backend="jax" tests)
+ test_package_with_jax:
+ runs-on: ${{ matrix.os }}
+ strategy:
+ fail-fast: false
+ matrix:
os: [ubuntu-latest, windows-latest, macos-latest]
python-version: ["3.11", "3.12", "3.13", "3.14"]
steps:
@@ -23,17 +24,41 @@ jobs:
- uses: ./.github/actions/test
with:
PYTHON_VERSION: ${{ matrix.python-version }}
-
+ INSTALL_JAX: 'true'
+
+ - name: Upload coverage to Coveralls
+ uses: coverallsapp/github-action@v2
+ with:
+ github-token: ${{ secrets.GITHUB_TOKEN }}
+ path-to-lcov: coverage.xml
+ flag-name: ${{ matrix.os }}-python-${{ matrix.python-version }}-jax
+ parallel: true
+
+ # Numpy-only installation (without JAX); backend="jax" tests are skipped
+ test_package_numpy_only:
+ runs-on: ${{ matrix.os }}
+ strategy:
+ fail-fast: false
+ matrix:
+ os: [ubuntu-latest, windows-latest, macos-latest]
+ python-version: ["3.11", "3.12", "3.13", "3.14"]
+ steps:
+ - uses: actions/checkout@v4
+ - uses: ./.github/actions/test
+ with:
+ PYTHON_VERSION: ${{ matrix.python-version }}
+ INSTALL_JAX: 'false'
+
- name: Upload coverage to Coveralls
uses: coverallsapp/github-action@v2
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
path-to-lcov: coverage.xml
- flag-name: ${{ matrix.os }}-python-${{ matrix.python-version }}
+ flag-name: ${{ matrix.os }}-python-${{ matrix.python-version }}-numpy-only
parallel: true
finish:
- needs: test_package
+ needs: [test_package_with_jax, test_package_numpy_only]
runs-on: ubuntu-latest
steps:
- name: Finish Coveralls
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 6960dd2..6b7f5fe 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,7 +5,19 @@ All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
-## [4.0.0] - 2025
+## [3.2.0] - 2026-06-29
+
+### Added
+- Optional **JAX backend** for `CVMatrix` via `backend="jax"` (a `Literal["numpy", "jax"]`; default remains `"numpy"`). All array operations are routed through a resolved array namespace (`numpy` or `jax.numpy`), so the per-fold `training_XTX`/`training_XTY`/`training_XTX_XTY`/`training_statistics` computations can be traced by `jax.jit` and batched with `jax.vmap` on CPU/GPU/TPU. Install with `cvmatrix[jax]`.
+- Backend-neutral, trace-safe standard-deviation clamping (`maximum`/`where` instead of boolean-mask assignment). Degenerate-fold validation (`ValueError`s) still fires for eager (numpy or concrete jax) execution; under `jax.jit`/`jax.vmap` tracing the check is deferred to a host-side pre-flight by the caller.
+
+### Changed
+- Switched build and development tooling from Poetry to [uv](https://docs.astral.sh/uv/): the project now uses PEP 621 `[project]` metadata, the `hatchling` build backend, and a PEP 735 `[dependency-groups]` dev group (`poetry.lock` is replaced by `uv.lock`). The published package, its runtime dependencies, and the `jax` extra are unchanged.
+
+### Notes
+- The numpy backend is byte-identical to previous releases (verified against the existing test suite) and performance-neutral.
+
+## [3.1.6] - 2025
### Changed
- `CVMatrix` and `Partitioner` can now be imported with `from cvmatrix import CVMatrix, Partitioner` or `import cvmatrix` followed by `cvmatrix.CVMatrix` and `cvmatrix.Partitioner`.
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 9b4eefc..afff81c 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -44,7 +44,7 @@ If you feel like you've made a valuable contribution, but you don't know how to
## Build from source
-CVMatrix uses [poetry](https://python-poetry.org/) to manage its dependencies and packaging. To build the package from source, follow these steps:
+CVMatrix uses [uv](https://docs.astral.sh/uv/) to manage its dependencies and packaging. To build the package from source, follow these steps:
1. Clone the repository:
@@ -58,57 +58,50 @@ CVMatrix uses [poetry](https://python-poetry.org/) to manage its dependencies an
cd CVMatrix
```
-3. Install poetry and twine:
+3. [Install uv](https://docs.astral.sh/uv/getting-started/installation/), e.g.:
```shell
- pip3 install poetry
- pip3 install twine
+ curl -LsSf https://astral.sh/uv/install.sh | sh
```
-4. Install the dependencies:
+4. Build the package. uv builds in an isolated environment, so no separate dependency-install step is needed:
```shell
- poetry install
+ uv build
```
-5. Build the package:
+5. Check the package with twine:
```shell
- poetry build
- ```
-
-6. Check the package with twine:
-
- ```shell
- twine check dist/*
+ uvx twine check dist/*
```
## Run the test suite
To run the test suite, follow these steps:
-1. Install poetry:
+1. [Install uv](https://docs.astral.sh/uv/getting-started/installation/), e.g.:
```shell
- pip3 install poetry
+ curl -LsSf https://astral.sh/uv/install.sh | sh
```
-2. Install the project dependencies with poetry:
+2. Install the project together with its development dependencies. To also exercise the optional JAX backend (the `backend="jax"` tests), include the `jax` extra:
```shell
- poetry install
+ uv sync --extra jax --group dev
```
-3. Install additional dependencies for testing:
+ To test the numpy-only installation instead (the `backend="jax"` tests are skipped automatically), omit the extra:
```shell
- poetry add --group dev pandas pytest flake8 pytest-cov typeguard
+ uv sync --group dev
```
-4. Now, the tests can be run with the following command:
+3. Now, the tests can be run with the following command:
```shell
- poetry run pytest tests --doctest-modules --junitxml=junit/test-results.xml --cov=cvmatrix/ --cov-report=xml --cov-report=html --typeguard-packages=cvmatrix/
+ uv run pytest tests --doctest-modules --junitxml=junit/test-results.xml --cov=cvmatrix/ --cov-report=xml --cov-report=html --typeguard-packages=cvmatrix/
```
## Build the documentation
diff --git a/README.md b/README.md
index d3b0139..e406827 100644
--- a/README.md
+++ b/README.md
@@ -36,6 +36,54 @@ The `cvmatrix` software package now also features **weigthed matrix produts** $\
from cvmatrix import CVMatrix
```
+- You can also install the optional JAX dependency to enable the JAX backend of
+ `CVMatrix`:
+ ```shell
+ pip3 install "cvmatrix[jax]"
+ ```
+- Now, you can select the JAX backend by passing `backend="jax"` to the constructor:
+ ```python
+ from cvmatrix import CVMatrix
+
+ cvm = CVMatrix(
+ center_X=True, center_Y=True, scale_X=True, scale_Y=True, backend="jax"
+ )
+ ```
+ With `backend="jax"`, the per-fold training-matrix computations (`training_XTX`,
+ `training_XTY`, `training_XTX_XTY`, and `training_statistics`) use `jax.numpy` and can
+ be traced by `jax.jit` and batched over folds with `jax.vmap` on CPUs, GPUs, and TPUs.
+ The default `backend="numpy"` is unchanged and remains byte-for-byte identical to
+ previous releases.
+
+### Prerequisites for the JAX backend
+
+The JAX backend supports running on CPU, GPU, and TPU.
+
+- To enable NVIDIA GPU execution, install JAX and CUDA with:
+ ```shell
+ pip3 install -U "jax[cuda13]"
+ ```
+
+- To enable Google Cloud TPU execution, install JAX with:
+ ```shell
+ pip3 install -U "jax[tpu]"
+ ```
+
+These are typical installation instructions that will be what most users are looking for.
+For customized installations, follow the instructions from the [JAX Installation
+Guide](https://jax.readthedocs.io/en/latest/installation.html).
+
+To ensure that the JAX backend uses float64, set the environment variable
+```JAX_ENABLE_X64=True``` as per the [Common
+Gotchas](https://docs.jax.dev/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision).
+`CVMatrix` also enables this automatically when constructed with `backend="jax"` and a
+64-bit `dtype` (the default). Alternatively, float64 can be enabled with the following
+function call:
+```python
+import jax
+jax.config.update("jax_enable_x64", True)
+```
+
## Quick Start
### Use the cvmatrix package for fast computation of training set kernel matrices
@@ -96,6 +144,7 @@ The `cvmatrix` software package now also features **weigthed matrix produts** $\
In [examples](https://github.com/Sm00thix/CVMatrix/tree/main/examples), you will find:
- [Compute training matrices with CVMatrix](https://github.com/Sm00thix/CVMatrix/tree/main/examples/training_matrices.py)
+- [Compute training matrices with the JAX backend, batched over folds with `jax.vmap` on CPU/GPU/TPU](https://github.com/Sm00thix/CVMatrix/tree/main/examples/training_matrices_jax.py)
## Benchmarks
@@ -107,6 +156,14 @@ In [benchmarks](https://github.com/Sm00thix/CVMatrix/tree/main/benchmarks), we h
Left: Benchmarking cross-validation with the CVMatrix implementation versus the baseline implementation using three common combinations of (column-wise) centering and scaling. Right: Benchmarking cross-validation with the CVMatrix implementation for all possible combinations of (column-wise) centering and scaling. Here, most of the graphs lie on top of eachother. In general, no preprocessing is faster than centering which, in turn, is faster than scaling.
+The figures below add the optional JAX backend to the comparison (NumPy vs. JAX, and the JAX execution modes). They are measured **multi-threaded** (no thread limit; GPU on an NVIDIA RTX 3090 Ti) and **exclude `NaiveCVMatrix`**:
+
+
+ 
+
+ Left: CVMatrix with the NumPy backend versus the JAX backend (warm, JIT-compiled jax.vmap over folds) on CPU and GPU, for the three common preprocessing combinations. Right: The JAX backend under no-JIT (eager vmap), cold-JIT (compilation + run) and warm-JIT (run only), on CPU and GPU. JIT compilation is a large fixed cost: on the GPU, no-JIT beats cold-JIT for a single run, whereas on the CPU, JIT (even cold) is far faster than eager vmap at a large number of folds.
+
+
## Contribute
To contribute, please read the [Contribution
diff --git a/benchmarks/benchmark.py b/benchmarks/benchmark.py
index 872878d..7638480 100644
--- a/benchmarks/benchmark.py
+++ b/benchmarks/benchmark.py
@@ -17,10 +17,8 @@
# Add the parent directory of 'CVMatrix' to sys.path
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-# Set the number of threads to 1. This must be done before importing numpy.
-os.environ["OMP_NUM_THREADS"] = "1"
-
from itertools import product
+from time import perf_counter
from timeit import timeit
from typing import Hashable, Iterable, Union
@@ -35,15 +33,15 @@
def save_result_to_csv(
model, use_weights, P, N, K, M, center_X, center_Y, scale_X, scale_Y, time, version
):
+ csv_path = os.environ.get("BENCH_CSV", "benchmark_results.csv")
try:
- with open("benchmark_results.csv", "x") as f:
+ with open(csv_path, "x") as f:
f.write(
- "model,weights,P,N,K,M,"
- "center_X,center_Y,scale_X,scale_Y,time,version\n"
+ "model,weights,P,N,K,M,center_X,center_Y,scale_X,scale_Y,time,version\n"
)
except FileExistsError:
pass
- with open("benchmark_results.csv", "a") as f:
+ with open(csv_path, "a") as f:
f.write(
f"{model},{use_weights},{P},{N},{K},{M},"
f"{center_X},{center_Y},{scale_X},{scale_Y},"
@@ -61,6 +59,8 @@ def execute_algorithm(
X: np.ndarray,
Y: np.ndarray,
weights: Union[None, np.ndarray],
+ backend: str = "numpy",
+ batch_size: int = 10000,
):
"""
Execute the computation of the training set matrices
@@ -98,15 +98,26 @@ def execute_algorithm(
The weights for the samples, if any. If None, no weights are used.
"""
- # Create the model
- model = model_class(
- center_X=center_X,
- center_Y=center_Y,
- scale_X=scale_X,
- scale_Y=scale_Y,
- dtype=X.dtype,
- copy=True,
- )
+ # Create the model. The CVMatrix model takes a `backend` ("numpy" or "jax").
+ if model_class is CVMatrix:
+ model = CVMatrix(
+ center_X=center_X,
+ center_Y=center_Y,
+ scale_X=scale_X,
+ scale_Y=scale_Y,
+ dtype=X.dtype,
+ copy=True,
+ backend=backend,
+ )
+ else:
+ model = model_class(
+ center_X=center_X,
+ center_Y=center_Y,
+ scale_X=scale_X,
+ scale_Y=scale_Y,
+ dtype=X.dtype,
+ copy=True,
+ )
# Create the validation partitioner
p = Partitioner(folds=cv_splits)
@@ -122,6 +133,23 @@ def execute_algorithm(
[p.get_validation_indices(f) for f in p.folds_dict if f != fold]
)
model.training_XTX_XTY(training_indices)
+ elif backend == "jax":
+ # Batch the per-fold training matrices over folds with jax.vmap (grouping by
+ # validation-set size so shapes are fixed, and chunking by `batch_size` to bound
+ # memory).
+ import jax
+ import jax.numpy as jnp
+ from collections import defaultdict
+
+ buckets: dict[int, list] = defaultdict(list)
+ for fold in p.folds_dict:
+ vi = p.get_validation_indices(fold)
+ buckets[vi.shape[0]].append(vi)
+ vmapped = jax.jit(jax.vmap(model.training_XTX_XTY))
+ for vis in buckets.values():
+ stack = jnp.asarray(np.stack(vis))
+ for s in range(0, len(vis), batch_size):
+ jax.block_until_ready(vmapped(stack[s : s + batch_size]))
else:
# Compute the training set matrices
for fold in p.folds_dict:
@@ -130,26 +158,130 @@ def execute_algorithm(
model.training_XTX_XTY(validation_indices)
+def benchmark_jax_variants(
+ cv_splits, center_X, center_Y, scale_X, scale_Y, X, Y, weights, batch_size
+):
+ """
+ Times the per-fold training-matrix computation (vmapped over folds, chunked by
+ `batch_size`) under three JAX execution modes, each including the one-time fit so the
+ result is the total cross-validation time (comparable to the numpy/naive totals):
+
+ - "nojit": eager ``jax.vmap`` (no JIT compilation).
+ - "coldjit": ``jax.jit(jax.vmap(...))`` timed on its first call (compilation + run).
+ - "warmjit": the same jitted function timed on a subsequent call (run only).
+
+ Returns a dict mapping each mode to its total time in seconds.
+ """
+ import jax
+ import jax.numpy as jnp
+ from collections import defaultdict
+
+ p = Partitioner(folds=cv_splits)
+
+ t0 = perf_counter()
+ model = CVMatrix(
+ center_X=center_X, center_Y=center_Y, scale_X=scale_X, scale_Y=scale_Y,
+ dtype=X.dtype, copy=True, backend="jax",
+ )
+ model.fit(X, Y, weights)
+ # Stack the per-fold validation indices into fixed-shape batches (one per fold size).
+ buckets = defaultdict(list)
+ for fold in p.folds_dict:
+ vi = p.get_validation_indices(fold)
+ buckets[vi.shape[0]].append(vi)
+ batches = [jnp.asarray(np.stack(vis)) for vis in buckets.values()]
+ fit_time = perf_counter() - t0
+
+ def run(vf):
+ for stack in batches:
+ for s in range(0, stack.shape[0], batch_size):
+ jax.block_until_ready(vf(stack[s : s + batch_size]))
+
+ # No JIT: eager vmap.
+ vf_nojit = jax.vmap(model.training_XTX_XTY)
+ t0 = perf_counter()
+ run(vf_nojit)
+ nojit = perf_counter() - t0
+
+ # JIT: the first call compiles (cold); a subsequent call reuses it (warm).
+ vf_jit = jax.jit(jax.vmap(model.training_XTX_XTY))
+ t0 = perf_counter()
+ run(vf_jit)
+ coldjit = perf_counter() - t0
+ t0 = perf_counter()
+ run(vf_jit)
+ warmjit = perf_counter() - t0
+
+ return {
+ "nojit": fit_time + nojit,
+ "coldjit": fit_time + coldjit,
+ "warmjit": fit_time + warmjit,
+ }
+
+
if __name__ == "__main__":
seed = 42 # Seed for reproducibility
rng = np.random.default_rng(seed=seed)
- N = 100000 # 100k samples
- K = 500 # 500 features
- M = 10 # 10 targets
+ N = int(os.environ.get("BENCH_N", 100000)) # 100k samples
+ K = int(os.environ.get("BENCH_K", 500)) # 500 features
+ M = int(os.environ.get("BENCH_M", 10)) # 10 targets
dtype = np.float64 # Data type
X = rng.random((N, K), dtype=dtype) # Random X matrix
Y = rng.random((N, M), dtype=dtype) # Random Y matrix
weights = rng.random((N,), dtype=dtype) # Random weights
cv_splits = np.arange(N) # We can use mod P for P-fold cross-validation
use_weights = [True, False] # Whether to use weights or not
- center_Xs = [True, False]
- center_Ys = [True, False]
- scale_Xs = [True, False]
- scale_Ys = [True, False]
- Ps = [3, 5, 10, 100, 1000, 10000, 100000]
-
- for use_w, center_X, center_Y, scale_X, scale_Y, P in product(
- use_weights, center_Xs, center_Ys, scale_Xs, scale_Ys, Ps
+ # Preprocessing combinations. BENCH_CONFIGS="plot" restricts to the three
+ # combinations shown in benchmark_cvmatrix_vs_naive.png (and the ones for which
+ # NaiveCVMatrix is benchmarked): no preprocessing, centering only, and centering +
+ # scaling. "all" (the default) sweeps every combination of the four flags.
+ if os.environ.get("BENCH_CONFIGS", "all") == "plot":
+ preprocessing_configs = [
+ (False, False, False, False),
+ (True, True, False, False),
+ (True, True, True, True),
+ ]
+ else:
+ preprocessing_configs = list(
+ product([True, False], [True, False], [True, False], [True, False])
+ )
+ # Ps = [3, 5, 10, 100, 1000, 10000, 100000]
+ Ps = [int(p) for p in os.environ.get("BENCH_PS", "100000").split(",")]
+ # CVMatrix backends to benchmark (comma-separated). "jax" requires cvmatrix[jax].
+ backends = os.environ.get("BENCH_BACKENDS", "numpy").split(",")
+ batch_size = int(os.environ.get("BENCH_BATCH", 10000))
+
+ # BENCH_JAX_VARIANTS=1: instead of the numpy/naive/jax sweep, benchmark the JAX
+ # backend under no-JIT / cold-JIT / warm-JIT (weighted only) for the figure
+ # benchmark_jax_variants.png.
+ if os.environ.get("BENCH_JAX_VARIANTS", "0") == "1":
+ import jax
+
+ platform = jax.devices()[0].platform
+ for (center_X, center_Y, scale_X, scale_Y), P in product(
+ preprocessing_configs, Ps
+ ):
+ print(
+ f"JAX variants ({platform}): P={P}, center_X={center_X}, "
+ f"center_Y={center_Y}, scale_X={scale_X}, scale_Y={scale_Y}"
+ )
+ mode_times = benchmark_jax_variants(
+ cv_splits % P, center_X, center_Y, scale_X, scale_Y,
+ X, Y, weights, batch_size,
+ )
+ for mode, t in mode_times.items():
+ print(f" CVMatrix-jax-{platform}-{mode}: {t:.2f} seconds")
+ save_result_to_csv(
+ f"CVMatrix-jax-{platform}-{mode}",
+ True,
+ P, N, K, M,
+ center_X, center_Y, scale_X, scale_Y,
+ t, __version__,
+ )
+ sys.exit(0)
+
+ for use_w, (center_X, center_Y, scale_X, scale_Y), P in product(
+ use_weights, preprocessing_configs, Ps
):
print(
f"weights={use_w}, "
@@ -157,40 +289,56 @@ def execute_algorithm(
f"center_X={center_X}, center_Y={center_Y}, "
f"scale_X={scale_X}, scale_Y={scale_Y}, "
)
- time = timeit(
- stmt=lambda: execute_algorithm(
- model_class=CVMatrix,
- cv_splits=cv_splits % P,
- center_X=center_X,
- center_Y=center_Y,
- scale_X=scale_X,
- scale_Y=scale_Y,
- X=X,
- Y=Y,
- weights=weights if use_weights else None,
- ),
- number=1,
- )
- print(f"CVMatrix, Time: {time:.2f} seconds")
- save_result_to_csv(
- "CVMatrix",
- use_w,
- P,
- N,
- K,
- M,
- center_X,
- center_Y,
- scale_X,
- scale_Y,
- time,
- __version__,
- )
+ for backend in backends:
+ time = timeit(
+ stmt=lambda backend=backend: execute_algorithm(
+ model_class=CVMatrix,
+ cv_splits=cv_splits % P,
+ center_X=center_X,
+ center_Y=center_Y,
+ scale_X=scale_X,
+ scale_Y=scale_Y,
+ X=X,
+ Y=Y,
+ weights=weights if use_w else None,
+ backend=backend,
+ batch_size=batch_size,
+ ),
+ number=1,
+ )
+ if backend == "jax":
+ import jax
+
+ # Distinguish CPU vs GPU/TPU JAX runs in the results.
+ model_name = f"CVMatrix-jax-{jax.devices()[0].platform}"
+ else:
+ model_name = (
+ "CVMatrix" if backend == "numpy" else f"CVMatrix-{backend}"
+ )
+ print(f"{model_name}, Time: {time:.2f} seconds")
+ save_result_to_csv(
+ model_name,
+ use_w,
+ P,
+ N,
+ K,
+ M,
+ center_X,
+ center_Y,
+ scale_X,
+ scale_Y,
+ time,
+ __version__,
+ )
- if (
+ # NaiveCVMatrix is only benchmarked for the three preprocessing combinations
+ # shown in benchmark_cvmatrix_vs_naive.png (no preprocessing, centering only, and
+ # centering + scaling). It is extremely slow (hours at large P), so it only runs
+ # when BENCH_NAIVE=1 is set explicitly.
+ if os.environ.get("BENCH_NAIVE", "0") == "1" and (
center_X == center_Y == scale_X == scale_Y
- or center_X == center_Y == True
- and scale_X == scale_Y == False
+ or center_X == center_Y is True
+ and scale_X == scale_Y is False
):
time = timeit(
stmt=lambda: execute_algorithm(
@@ -202,7 +350,7 @@ def execute_algorithm(
scale_Y=scale_Y,
X=X,
Y=Y,
- weights=weights,
+ weights=weights if use_w else None,
),
number=1,
)
diff --git a/benchmarks/benchmark_cvmatrix_numpy_vs_jax.png b/benchmarks/benchmark_cvmatrix_numpy_vs_jax.png
new file mode 100644
index 0000000..7aa4e10
Binary files /dev/null and b/benchmarks/benchmark_cvmatrix_numpy_vs_jax.png differ
diff --git a/benchmarks/benchmark_jax_variants.png b/benchmarks/benchmark_jax_variants.png
new file mode 100644
index 0000000..6cbc718
Binary files /dev/null and b/benchmarks/benchmark_jax_variants.png differ
diff --git a/benchmarks/benchmark_results.csv b/benchmarks/benchmark_results.csv
index b288dec..bb767dd 100644
--- a/benchmarks/benchmark_results.csv
+++ b/benchmarks/benchmark_results.csv
@@ -1,134 +1,267 @@
-model,P,N,K,M,center_X,center_Y,scale_X,scale_Y,time,version
-CVMatrix,3,100000,500,10,True,True,True,True,1.1192415489999803,1.0.0
-NaiveCVMatrix,3,100000,500,10,True,True,True,True,1.525318347999928,1.0.0
-CVMatrix,5,100000,500,10,True,True,True,True,1.1311610759998985,1.0.0
-NaiveCVMatrix,5,100000,500,10,True,True,True,True,3.018902382000306,1.0.0
-CVMatrix,10,100000,500,10,True,True,True,True,1.1376194539998323,1.0.0
-NaiveCVMatrix,10,100000,500,10,True,True,True,True,6.742205670000203,1.0.0
-CVMatrix,100,100000,500,10,True,True,True,True,1.1367952779996813,1.0.0
-NaiveCVMatrix,100,100000,500,10,True,True,True,True,73.3318574079999,1.0.0
-CVMatrix,1000,100000,500,10,True,True,True,True,1.7896336470003007,1.0.0
-NaiveCVMatrix,1000,100000,500,10,True,True,True,True,729.6242519150001,1.0.0
-CVMatrix,10000,100000,500,10,True,True,True,True,8.568363662000138,1.0.0
-NaiveCVMatrix,10000,100000,500,10,True,True,True,True,7256.869186553,1.0.0
-CVMatrix,100000,100000,500,10,True,True,True,True,87.74830843000018,1.0.0
-NaiveCVMatrix,100000,100000,500,10,True,True,True,True,73826.453205139,1.0.0
-CVMatrix,3,100000,500,10,True,True,True,False,1.118339446009486,1.0.0
-CVMatrix,5,100000,500,10,True,True,True,False,1.123899715996231,1.0.0
-CVMatrix,10,100000,500,10,True,True,True,False,1.12319784500869,1.0.0
-CVMatrix,100,100000,500,10,True,True,True,False,1.114717668009689,1.0.0
-CVMatrix,1000,100000,500,10,True,True,True,False,1.7559398589946795,1.0.0
-CVMatrix,10000,100000,500,10,True,True,True,False,8.289280587996473,1.0.0
-CVMatrix,100000,100000,500,10,True,True,True,False,86.02160608601116,1.0.0
-CVMatrix,3,100000,500,10,True,True,False,True,1.0702028819941916,1.0.0
-CVMatrix,5,100000,500,10,True,True,False,True,1.0765490460034923,1.0.0
-CVMatrix,10,100000,500,10,True,True,False,True,1.0745869239908643,1.0.0
-CVMatrix,100,100000,500,10,True,True,False,True,1.0526132069935557,1.0.0
-CVMatrix,1000,100000,500,10,True,True,False,True,1.4689054019981995,1.0.0
-CVMatrix,10000,100000,500,10,True,True,False,True,5.673868882993702,1.0.0
-CVMatrix,100000,100000,500,10,True,True,False,True,59.158317786990665,1.0.0
-CVMatrix,3,100000,500,10,True,True,False,False,1.071286210004473,1.0.0
-NaiveCVMatrix,3,100000,500,10,True,True,False,False,1.2249300480034435,1.0.0
-CVMatrix,5,100000,500,10,True,True,False,False,1.0742946690006647,1.0.0
-NaiveCVMatrix,5,100000,500,10,True,True,False,False,2.3995333990023937,1.0.0
-CVMatrix,10,100000,500,10,True,True,False,False,1.0759979769936765,1.0.0
-NaiveCVMatrix,10,100000,500,10,True,True,False,False,5.318012420000741,1.0.0
-CVMatrix,100,100000,500,10,True,True,False,False,1.0516475519980304,1.0.0
-NaiveCVMatrix,100,100000,500,10,True,True,False,False,57.35077489100513,1.0.0
-CVMatrix,1000,100000,500,10,True,True,False,False,1.4252776270004688,1.0.0
-NaiveCVMatrix,1000,100000,500,10,True,True,False,False,578.4537375619984,1.0.0
-CVMatrix,10000,100000,500,10,True,True,False,False,5.3335611680086,1.0.0
-NaiveCVMatrix,10000,100000,500,10,True,True,False,False,5781.912272988004,1.0.0
-CVMatrix,100000,100000,500,10,True,True,False,False,56.71489888700307,1.0.0
-NaiveCVMatrix,100000,100000,500,10,True,True,False,False,58952.35878656501,1.0.0
-CVMatrix,3,100000,500,10,True,False,True,True,1.1248798799933866,1.0.0
-CVMatrix,5,100000,500,10,True,False,True,True,1.1283268360130023,1.0.0
-CVMatrix,10,100000,500,10,True,False,True,True,1.1263538440107368,1.0.0
-CVMatrix,100,100000,500,10,True,False,True,True,1.119193897000514,1.0.0
-CVMatrix,1000,100000,500,10,True,False,True,True,1.7814686659839936,1.0.0
-CVMatrix,10000,100000,500,10,True,False,True,True,8.538902194995899,1.0.0
-CVMatrix,100000,100000,500,10,True,False,True,True,87.61705726798391,1.0.0
-CVMatrix,3,100000,500,10,True,False,True,False,1.1210701089876238,1.0.0
-CVMatrix,5,100000,500,10,True,False,True,False,1.1266690100019332,1.0.0
-CVMatrix,10,100000,500,10,True,False,True,False,1.1292253129940946,1.0.0
-CVMatrix,100,100000,500,10,True,False,True,False,1.1189919460157398,1.0.0
-CVMatrix,1000,100000,500,10,True,False,True,False,1.7613697280175984,1.0.0
-CVMatrix,10000,100000,500,10,True,False,True,False,8.302096697007073,1.0.0
-CVMatrix,100000,100000,500,10,True,False,True,False,86.14565880500595,1.0.0
-CVMatrix,3,100000,500,10,True,False,False,True,1.0740263529878575,1.0.0
-CVMatrix,5,100000,500,10,True,False,False,True,1.072119502001442,1.0.0
-CVMatrix,10,100000,500,10,True,False,False,True,1.069851884996751,1.0.0
-CVMatrix,100,100000,500,10,True,False,False,True,1.0546411169925705,1.0.0
-CVMatrix,1000,100000,500,10,True,False,False,True,1.4577812999777962,1.0.0
-CVMatrix,10000,100000,500,10,True,False,False,True,5.598208290990442,1.0.0
-CVMatrix,100000,100000,500,10,True,False,False,True,59.14233927798341,1.0.0
-CVMatrix,3,100000,500,10,True,False,False,False,1.072362840990536,1.0.0
-CVMatrix,5,100000,500,10,True,False,False,False,1.068599329009885,1.0.0
-CVMatrix,10,100000,500,10,True,False,False,False,1.0694805849925615,1.0.0
-CVMatrix,100,100000,500,10,True,False,False,False,1.051443169999402,1.0.0
-CVMatrix,1000,100000,500,10,True,False,False,False,1.4250595640041863,1.0.0
-CVMatrix,10000,100000,500,10,True,False,False,False,5.306501427985495,1.0.0
-CVMatrix,100000,100000,500,10,True,False,False,False,56.23384317601449,1.0.0
-CVMatrix,3,100000,500,10,False,True,True,True,1.1242727210046723,1.0.0
-CVMatrix,5,100000,500,10,False,True,True,True,1.1221562070131768,1.0.0
-CVMatrix,10,100000,500,10,False,True,True,True,1.1232905049982946,1.0.0
-CVMatrix,100,100000,500,10,False,True,True,True,1.093528530007461,1.0.0
-CVMatrix,1000,100000,500,10,False,True,True,True,1.50849640899105,1.0.0
-CVMatrix,10000,100000,500,10,False,True,True,True,5.678116741997656,1.0.0
-CVMatrix,100000,100000,500,10,False,True,True,True,59.44565243599936,1.0.0
-CVMatrix,3,100000,500,10,False,True,True,False,1.1244757279928308,1.0.0
-CVMatrix,5,100000,500,10,False,True,True,False,1.1264495220093522,1.0.0
-CVMatrix,10,100000,500,10,False,True,True,False,1.1244927080115303,1.0.0
-CVMatrix,100,100000,500,10,False,True,True,False,1.0898696849762928,1.0.0
-CVMatrix,1000,100000,500,10,False,True,True,False,1.487639525992563,1.0.0
-CVMatrix,10000,100000,500,10,False,True,True,False,5.532944937003776,1.0.0
-CVMatrix,100000,100000,500,10,False,True,True,False,57.769016504025785,1.0.0
-CVMatrix,3,100000,500,10,False,True,False,True,1.0699797239794862,1.0.0
-CVMatrix,5,100000,500,10,False,True,False,True,1.076646833011182,1.0.0
-CVMatrix,10,100000,500,10,False,True,False,True,1.066042849997757,1.0.0
-CVMatrix,100,100000,500,10,False,True,False,True,1.022873626992805,1.0.0
-CVMatrix,1000,100000,500,10,False,True,False,True,1.1741839119931683,1.0.0
-CVMatrix,10000,100000,500,10,False,True,False,True,2.767864119989099,1.0.0
-CVMatrix,100000,100000,500,10,False,True,False,True,31.288429460983025,1.0.0
-CVMatrix,3,100000,500,10,False,True,False,False,1.067737389006652,1.0.0
-CVMatrix,5,100000,500,10,False,True,False,False,1.0645694179984275,1.0.0
-CVMatrix,10,100000,500,10,False,True,False,False,1.0654148519970477,1.0.0
-CVMatrix,100,100000,500,10,False,True,False,False,1.0180366100103129,1.0.0
-CVMatrix,1000,100000,500,10,False,True,False,False,1.1448387459968217,1.0.0
-CVMatrix,10000,100000,500,10,False,True,False,False,2.4890578409831505,1.0.0
-CVMatrix,100000,100000,500,10,False,True,False,False,28.57835416399757,1.0.0
-CVMatrix,3,100000,500,10,False,False,True,True,1.1212003620166795,1.0.0
-CVMatrix,5,100000,500,10,False,False,True,True,1.1258387090056203,1.0.0
-CVMatrix,10,100000,500,10,False,False,True,True,1.128786824992858,1.0.0
-CVMatrix,100,100000,500,10,False,False,True,True,1.0982693690166343,1.0.0
-CVMatrix,1000,100000,500,10,False,False,True,True,1.4995669290074147,1.0.0
-CVMatrix,10000,100000,500,10,False,False,True,True,5.570729988015955,1.0.0
-CVMatrix,100000,100000,500,10,False,False,True,True,58.68603180799983,1.0.0
-CVMatrix,3,100000,500,10,False,False,True,False,1.1134203580149915,1.0.0
-CVMatrix,5,100000,500,10,False,False,True,False,1.1239128790039103,1.0.0
-CVMatrix,10,100000,500,10,False,False,True,False,1.1241297630185727,1.0.0
-CVMatrix,100,100000,500,10,False,False,True,False,1.087912362010684,1.0.0
-CVMatrix,1000,100000,500,10,False,False,True,False,1.4660285169957206,1.0.0
-CVMatrix,10000,100000,500,10,False,False,True,False,5.316281512990827,1.0.0
-CVMatrix,100000,100000,500,10,False,False,True,False,56.17939067899715,1.0.0
-CVMatrix,3,100000,500,10,False,False,False,True,1.0351853250176646,1.0.0
-CVMatrix,5,100000,500,10,False,False,False,True,1.033534198009875,1.0.0
-CVMatrix,10,100000,500,10,False,False,False,True,1.0377773079962935,1.0.0
-CVMatrix,100,100000,500,10,False,False,False,True,0.9942518580064644,1.0.0
-CVMatrix,1000,100000,500,10,False,False,False,True,1.1315311660000589,1.0.0
-CVMatrix,10000,100000,500,10,False,False,False,True,2.5680760239774827,1.0.0
-CVMatrix,100000,100000,500,10,False,False,False,True,29.533790613000747,1.0.0
-CVMatrix,3,100000,500,10,False,False,False,False,1.0287453010096217,1.0.0
-NaiveCVMatrix,3,100000,500,10,False,False,False,False,1.0761993249761872,1.0.0
-CVMatrix,5,100000,500,10,False,False,False,False,1.0289292840170674,1.0.0
-NaiveCVMatrix,5,100000,500,10,False,False,False,False,2.1083633030066267,1.0.0
-CVMatrix,10,100000,500,10,False,False,False,False,1.0330040709814057,1.0.0
-NaiveCVMatrix,10,100000,500,10,False,False,False,False,4.668692281993572,1.0.0
-CVMatrix,100,100000,500,10,False,False,False,False,0.9845351910043972,1.0.0
-NaiveCVMatrix,100,100000,500,10,False,False,False,False,50.40878304699436,1.0.0
-CVMatrix,1000,100000,500,10,False,False,False,False,1.0881256299908273,1.0.0
-NaiveCVMatrix,1000,100000,500,10,False,False,False,False,506.2128036149952,1.0.0
-CVMatrix,10000,100000,500,10,False,False,False,False,2.194315168017056,1.0.0
-NaiveCVMatrix,10000,100000,500,10,False,False,False,False,5044.3570687549945,1.0.0
-CVMatrix,100000,100000,500,10,False,False,False,False,26.013640838005813,1.0.0
-NaiveCVMatrix,100000,100000,500,10,False,False,False,False,52100.47697248199,1.0.0
+model,weights,P,N,K,M,center_X,center_Y,scale_X,scale_Y,time,version
+CVMatrix,True,3,100000,500,10,True,True,True,True,1.984333821994369,3.1.2
+NaiveCVMatrix,True,3,100000,500,10,True,True,True,True,2.391529404994799,3.1.2
+CVMatrix,True,5,100000,500,10,True,True,True,True,2.0060565159947146,3.1.2
+NaiveCVMatrix,True,5,100000,500,10,True,True,True,True,4.756514220003737,3.1.2
+CVMatrix,True,10,100000,500,10,True,True,True,True,2.0229949439963093,3.1.2
+NaiveCVMatrix,True,10,100000,500,10,True,True,True,True,10.702684000993031,3.1.2
+CVMatrix,True,100,100000,500,10,True,True,True,True,1.9863586209976347,3.1.2
+NaiveCVMatrix,True,100,100000,500,10,True,True,True,True,116.75834354999824,3.1.2
+CVMatrix,True,1000,100000,500,10,True,True,True,True,2.696976027000346,3.1.2
+NaiveCVMatrix,True,1000,100000,500,10,True,True,True,True,1167.3831180050038,3.1.2
+CVMatrix,True,10000,100000,500,10,True,True,True,True,10.405765339004574,3.1.2
+NaiveCVMatrix,True,10000,100000,500,10,True,True,True,True,11684.97914216001,3.1.2
+CVMatrix,True,100000,100000,500,10,True,True,True,True,97.79410633000953,3.1.2
+NaiveCVMatrix,True,100000,100000,500,10,True,True,True,True,117450.870239473,3.1.2
+CVMatrix,True,3,100000,500,10,True,True,True,False,1.9866961159859784,3.1.2
+CVMatrix,True,5,100000,500,10,True,True,True,False,1.9947043650026899,3.1.2
+CVMatrix,True,10,100000,500,10,True,True,True,False,2.0186615100246854,3.1.2
+CVMatrix,True,100,100000,500,10,True,True,True,False,1.9616220210155006,3.1.2
+CVMatrix,True,1000,100000,500,10,True,True,True,False,2.6776564110186882,3.1.2
+CVMatrix,True,10000,100000,500,10,True,True,True,False,10.229800548026105,3.1.2
+CVMatrix,True,100000,100000,500,10,True,True,True,False,96.38440647299285,3.1.2
+CVMatrix,True,3,100000,500,10,True,True,False,True,1.8646600369829684,3.1.2
+CVMatrix,True,5,100000,500,10,True,True,False,True,1.8728829300089274,3.1.2
+CVMatrix,True,10,100000,500,10,True,True,False,True,1.8833721109840553,3.1.2
+CVMatrix,True,100,100000,500,10,True,True,False,True,1.8414228140027262,3.1.2
+CVMatrix,True,1000,100000,500,10,True,True,False,True,2.2803589590184856,3.1.2
+CVMatrix,True,10000,100000,500,10,True,True,False,True,7.142492076003691,3.1.2
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\ No newline at end of file
diff --git a/benchmarks/benchmark_results_jax.csv b/benchmarks/benchmark_results_jax.csv
new file mode 100644
index 0000000..4ca2bb8
--- /dev/null
+++ b/benchmarks/benchmark_results_jax.csv
@@ -0,0 +1,169 @@
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+CVMatrix-jax-cpu-nojit,True,10,100000,500,10,True,True,True,True,1.9614091481780633,3.2.0
+CVMatrix-jax-cpu-coldjit,True,10,100000,500,10,True,True,True,True,8.78678807709366,3.2.0
+CVMatrix-jax-cpu-warmjit,True,10,100000,500,10,True,True,True,True,0.6999291750835255,3.2.0
+CVMatrix-jax-cpu-nojit,True,100,100000,500,10,True,True,True,True,2.295204345951788,3.2.0
+CVMatrix-jax-cpu-coldjit,True,100,100000,500,10,True,True,True,True,8.709234067006037,3.2.0
+CVMatrix-jax-cpu-warmjit,True,100,100000,500,10,True,True,True,True,0.7009000619873405,3.2.0
+CVMatrix-jax-cpu-nojit,True,1000,100000,500,10,True,True,True,True,4.529199573909864,3.2.0
+CVMatrix-jax-cpu-coldjit,True,1000,100000,500,10,True,True,True,True,9.04653775296174,3.2.0
+CVMatrix-jax-cpu-warmjit,True,1000,100000,500,10,True,True,True,True,1.0235839679371566,3.2.0
+CVMatrix-jax-cpu-nojit,True,10000,100000,500,10,True,True,True,True,24.003119080909528,3.2.0
+CVMatrix-jax-cpu-coldjit,True,10000,100000,500,10,True,True,True,True,12.497502946993336,3.2.0
+CVMatrix-jax-cpu-warmjit,True,10000,100000,500,10,True,True,True,True,4.75980460701976,3.2.0
+CVMatrix-jax-cpu-nojit,True,100000,100000,500,10,True,True,True,True,197.2365424539894,3.2.0
+CVMatrix-jax-cpu-coldjit,True,100000,100000,500,10,True,True,True,True,33.92279968911316,3.2.0
+CVMatrix-jax-cpu-warmjit,True,100000,100000,500,10,True,True,True,True,25.930547127034515,3.2.0
diff --git a/benchmarks/plot_benchmark.py b/benchmarks/plot_benchmark.py
index 2f075da..27f5f5b 100644
--- a/benchmarks/plot_benchmark.py
+++ b/benchmarks/plot_benchmark.py
@@ -11,6 +11,7 @@
E-mail: ole.e@di.ku.dk
"""
+import os
from itertools import product
import matplotlib.pyplot as plt
@@ -20,23 +21,28 @@
from matplotlib.text import Text
-def plot_cvmatrix_vs_naive(df, combination_to_color_map):
+def plot_cvmatrix_vs_naive(df, combination_to_color_map, weights):
+ # Set text and label sizes to 20
+ plt.rcParams.update({'font.size': 15, 'axes.labelsize': 15, 'axes.titlesize': 20, 'legend.fontsize': 15})
fig, ax = plt.subplots(figsize=(10, 10))
preprocessing_combinations = []
preprocessing_combinations.append(
- {'center_X': False, 'center_Y': False, 'scale_X': False, 'scale_Y': False}
+ {'center_X': False, 'center_Y': False, 'scale_X': False, 'scale_Y': False, 'weights': weights}
)
preprocessing_combinations.append(
- {'center_X': True, 'center_Y': True, 'scale_X': False, 'scale_Y': False}
+ {'center_X': True, 'center_Y': True, 'scale_X': False, 'scale_Y': False, 'weights': weights}
)
preprocessing_combinations.append(
- {'center_X': True, 'center_Y': True, 'scale_X': True, 'scale_Y': True}
+ {'center_X': True, 'center_Y': True, 'scale_X': True, 'scale_Y': True, 'weights': weights}
)
N = df['N'].unique()[0]
K = df['K'].unique()[0]
M = df['M'].unique()[0]
for combination in preprocessing_combinations:
- color = combination_to_color_map[str(combination)]
+ # Do not use the 'weights' key in combination when getting color
+ combination_no_weights = {k: v for k, v in combination.items() if k != 'weights'}
+ color = combination_to_color_map[str(combination_no_weights)]
+
fast_times = []
naive_times = []
Ps = []
@@ -47,7 +53,8 @@ def plot_cvmatrix_vs_naive(df, combination_to_color_map):
(df['center_X'] == combination['center_X']) &
(df['center_Y'] == combination['center_Y']) &
(df['scale_X'] == combination['scale_X']) &
- (df['scale_Y'] == combination['scale_Y'])
+ (df['scale_Y'] == combination['scale_Y']) &
+ (df['weights'] == combination['weights'])
]['time'].values[0]
naive_time = df[
(df['model'] == 'NaiveCVMatrix') &
@@ -55,29 +62,42 @@ def plot_cvmatrix_vs_naive(df, combination_to_color_map):
(df['center_X'] == combination['center_X']) &
(df['center_Y'] == combination['center_Y']) &
(df['scale_X'] == combination['scale_X']) &
- (df['scale_Y'] == combination['scale_Y'])
+ (df['scale_Y'] == combination['scale_Y']) &
+ (df['weights'] == combination['weights'])
]['time'].values[0]
fast_times.append(fast_time)
naive_times.append(naive_time)
Ps.append(P)
- label = "Fast (CVMatrix), " + ', '.join([f"{k}={v}" for k, v in combination.items()])
+ # label = "Baseline, " + ', '.join(
+ # [f"{k}={v}" for k, v in combination.items() if k != 'weights']
+ # )
+ if combination['scale_X']:
+ label = "Algorithm 6"
+ elif combination['center_X']:
+ label = "Algorithm 4"
+ else:
+ label = "Algorithm 2"
ax.plot(
Ps,
- fast_times,
- marker='D',
+ naive_times,
+ marker='s',
color=color,
- linestyle='dashed',
+ linestyle='dotted',
label=label
)
- label = "Baseline, " + ', '.join(
- [f"{k}={v}" for k, v in combination.items()]
- )
+ # label = "Fast (CVMatrix), " + ', '.join([f"{k}={v}" for k, v in combination.items() if k != 'weights'])
+ if combination['scale_X']:
+ label = "Algorithm 7"
+ elif combination['center_X']:
+ label = "Algorithm 5"
+ else:
+ label = "Algorithm 3"
ax.plot(
Ps,
- naive_times,
- marker='s',
+ fast_times,
+ marker='D',
color=color,
- linestyle='dotted',
+ linestyle='dashed',
label=label
)
lines = [1, 60, 3600, 86400]
@@ -85,11 +105,11 @@ def plot_cvmatrix_vs_naive(df, combination_to_color_map):
for j, line in enumerate(lines):
ax.axhline(y=line, color='k', linestyle='--', linewidth=1)
ax.text(
- 310000, line, f"1 {line_names[j]}", fontsize=10, ha='center', va='center'
+ 310000, line, f"1 {line_names[j]}", fontsize=12, ha='center', va='center'
)
cvmatrix_version = df['version'].unique()[0]
- version_text = f'CVMatrix version: {cvmatrix_version}'
- ax.text(1, 330000, version_text, fontsize=9, ha='center', va='center')
+ version_text = f'cvmatrix version: {cvmatrix_version}'
+ ax.text(1, 330000, version_text, fontsize=10, ha='center', va='center')
ax.set_xscale('log')
ax.set_yscale('log')
current_x_ticks, current_x_labels = plt.xticks()
@@ -106,16 +126,20 @@ def plot_cvmatrix_vs_naive(df, combination_to_color_map):
new_x_labels = new_x_labels[start_idx:stop_idx]
ax.set_xticks(new_x_ticks)
ax.set_xticklabels(new_x_labels)
- ax.set_title(f'Fast (CVMatrix) vs. Baseline Cross-Validation\n(N={N:,}, K={K}, M={M})', fontsize=10)
+ ax.set_title(f'Fast (cvmatrix) vs. Baseline Weighted Cross-Validation\n(N={N:,}, K={K}, M={M})', fontsize=20)
ax.set_xlabel('P (cross-validation folds)')
ax.set_ylabel('Time (s)')
- fig.set_size_inches(8.27, 0.37 * 11.7)
- ax.legend(loc='upper left', fontsize='xx-small')
- plt.tight_layout()
- plt.savefig('benchmark_cvmatrix_vs_naive.png')
+ # fig.set_size_inches(8.27, 0.37 * 11.7)
+ ax.legend(loc='upper left')#, fontsize='xx-small')
+ # plt.tight_layout()
+ if weights:
+ plt.savefig('benchmark_cvmatrix_vs_naive_weights.png')
+ plt.savefig('benchmark_cvmatrix_vs_naive_weights.eps')
+ else:
+ plt.savefig('benchmark_cvmatrix_vs_naive_no_weights.png')
plt.clf()
-def plot_cvmatrix(df, combination_to_color_map):
+def plot_cvmatrix(df, combination_to_color_map, weights):
fig, ax = plt.subplots(figsize=(10, 10))
preprocessing_combinations = []
center_Xs = [False, True]
@@ -129,14 +153,16 @@ def plot_cvmatrix(df, combination_to_color_map):
'center_X': center_X,
'center_Y': center_Y,
'scale_X': scale_X,
- 'scale_Y': scale_Y
+ 'scale_Y': scale_Y,
+ 'weights': weights
}
)
N = df['N'].unique()[0]
K = df['K'].unique()[0]
M = df['M'].unique()[0]
for combination in preprocessing_combinations:
- color = combination_to_color_map[str(combination)]
+ combination_no_weights = {k: v for k, v in combination.items() if k != 'weights'}
+ color = combination_to_color_map[str(combination_no_weights)]
fast_times = []
Ps = []
for P in df['P'].unique():
@@ -146,11 +172,12 @@ def plot_cvmatrix(df, combination_to_color_map):
(df['center_X'] == combination['center_X']) &
(df['center_Y'] == combination['center_Y']) &
(df['scale_X'] == combination['scale_X']) &
- (df['scale_Y'] == combination['scale_Y'])
+ (df['scale_Y'] == combination['scale_Y']) &
+ (df['weights'] == combination['weights'])
]['time'].values[0]
fast_times.append(fast_time)
Ps.append(P)
- label = ', '.join([f"{k}={v}" for k, v in combination.items()])
+ label = ', '.join([f"{k}={v}" for k, v in combination.items() if k != 'weights'])
ax.plot(
Ps,
fast_times,
@@ -164,11 +191,11 @@ def plot_cvmatrix(df, combination_to_color_map):
for j, line in enumerate(lines):
ax.axhline(y=line, color='k', linestyle='--', linewidth=1)
ax.text(
- 310000,line, f"1 {line_names[j]}", fontsize=10, ha='center', va='center'
+ 310000,line, f"1 {line_names[j]}", fontsize=20, ha='center', va='center'
)
cvmatrix_version = df['version'].unique()[0]
version_text = f'CVMatrix version: {cvmatrix_version}'
- ax.text(1, 330000, version_text, fontsize=9, ha='center', va='center')
+ ax.text(1, 330000, version_text, fontsize=20, ha='center', va='center')
ax.set_xscale('log')
ax.set_yscale('log')
current_x_ticks, current_x_labels = plt.xticks()
@@ -191,9 +218,155 @@ def plot_cvmatrix(df, combination_to_color_map):
fig.set_size_inches(8.27, 0.37 * 11.7)
ax.legend(loc='upper left', fontsize='xx-small')
plt.tight_layout()
- plt.savefig('benchmark_cvmatrix.png')
+ if weights:
+ plt.savefig('benchmark_cvmatrix_weights.png')
+ else:
+ plt.savefig('benchmark_cvmatrix_no_weights.png')
+ plt.clf()
+
+def _add_time_reflines(ax, ymax):
+ """Draw "1 Second/Minute/Hour/Day" reference lines up to (a little above) ymax."""
+ from matplotlib.text import Text # noqa: F401 (kept for parity with other plots)
+ for y, name in [(1, 'Second'), (60, 'Minute'), (3600, 'Hour'), (86400, 'Day')]:
+ if y <= ymax * 2:
+ ax.axhline(y=y, color='k', linestyle='--', linewidth=1)
+ ax.text(310000, y, f'1 {name}', fontsize=11, ha='center', va='center')
+
+
+_COMBOS = [
+ ((False, False, False, False), 'solid', 'no preprocessing'),
+ ((True, True, False, False), 'dashed', 'centering'),
+ ((True, True, True, True), 'dotted', 'centering + scaling'),
+]
+
+
+def _combo_dict(key):
+ return dict(zip(('center_X', 'center_Y', 'scale_X', 'scale_Y'), key))
+
+
+def _series_times(df, model, key, Ps_all):
+ """Per-P times (weighted) for a given model and (center_X, center_Y, scale_X,
+ scale_Y) combination; returns (Ps, times) over the P values that have a row."""
+ cX, cY, sX, sY = key
+ times, Ps = [], []
+ for P in Ps_all:
+ sel = df[
+ (df['model'] == model) & (df['P'] == P) & (df['weights'] == True)
+ & (df['center_X'] == cX) & (df['center_Y'] == cY)
+ & (df['scale_X'] == sX) & (df['scale_Y'] == sY)
+ ]['time'].values
+ if len(sel):
+ times.append(sel[0])
+ Ps.append(P)
+ return Ps, times
+
+
+def plot_numpy_vs_jax(df, combination_to_color_map):
+ """
+ benchmark_cvmatrix_numpy_vs_jax.png: total weighted cross-validation time for the
+ CVMatrix NumPy backend vs. the JAX backend (warm, JIT-compiled jax.vmap over folds) on
+ CPU and GPU, across the three preprocessing combinations. All measured multi-threaded
+ (no thread limit) at cvmatrix 3.2.0. Color encodes preprocessing; line style encodes
+ backend.
+ """
+ from matplotlib.lines import Line2D
+ plt.rcParams.update({'font.size': 15, 'axes.labelsize': 15,
+ 'axes.titlesize': 18, 'legend.fontsize': 11})
+ fig, ax = plt.subplots(figsize=(10, 10))
+ # (model, linestyle, marker, legend label)
+ series = [
+ ('CVMatrix', 'dashed', 'D', 'NumPy'),
+ ('CVMatrix-jax-cpu-warmjit', 'dashdot', '^', 'JAX warm (CPU)'),
+ ('CVMatrix-jax-gpu-warmjit', 'solid', 'o', 'JAX warm (GPU)'),
+ ]
+ N = df['N'].unique()[0]
+ K = df['K'].unique()[0]
+ M = df['M'].unique()[0]
+ Ps_all = sorted(df['P'].unique())
+ ymax = 0.0
+ for key, _ls, combo_lab in _COMBOS:
+ color = combination_to_color_map[str(_combo_dict(key))]
+ for model, linestyle, marker, _lab in series:
+ Ps, times = _series_times(df, model, key, Ps_all)
+ if times:
+ ax.plot(Ps, times, marker=marker, color=color, linestyle=linestyle)
+ ymax = max(ymax, max(times))
+ _add_time_reflines(ax, ymax)
+ ax.set_xscale('log')
+ ax.set_yscale('log')
+ ax.set_title('CVMatrix NumPy vs. JAX (warm) Weighted Cross-Validation\n'
+ f'(N={N:,}, K={K}, M={M})', fontsize=18)
+ ax.set_xlabel('P (cross-validation folds)')
+ ax.set_ylabel('Time (s)')
+ backend_handles = [Line2D([0], [0], color='gray', linestyle=ls, marker=mk, label=lab)
+ for _m, ls, mk, lab in series]
+ combo_handles = [Line2D([0], [0], color=combination_to_color_map[str(_combo_dict(k))],
+ lw=3, label=lab) for k, _ls, lab in _COMBOS]
+ leg1 = ax.legend(handles=backend_handles, loc='upper left', title='Backend')
+ ax.add_artist(leg1)
+ ax.legend(handles=combo_handles, loc='lower right', title='Preprocessing')
+ fig.text(0.5, 0.01, 'cvmatrix 3.2.0; multi-threaded CPU; GPU: RTX 3090 Ti',
+ ha='center', va='bottom', fontsize=9, alpha=0.7)
+ plt.tight_layout(rect=(0, 0.03, 1, 1))
+ plt.savefig('benchmark_cvmatrix_numpy_vs_jax.png')
plt.clf()
+
+def plot_jax_variants(df):
+ """
+ benchmark_jax_variants.png: total weighted cross-validation time for the CVMatrix JAX
+ backend under no-JIT (eager vmap), cold-JIT (compilation + run) and warm-JIT (run
+ only), on CPU and GPU, across the three preprocessing combinations. Multi-threaded
+ CPU, cvmatrix 3.2.0. Color encodes the JAX mode/device; line style encodes
+ preprocessing.
+ """
+ from matplotlib.lines import Line2D
+ plt.rcParams.update({'font.size': 15, 'axes.labelsize': 15,
+ 'axes.titlesize': 18, 'legend.fontsize': 10})
+ fig, ax = plt.subplots(figsize=(10, 10))
+ variants = [
+ ('CVMatrix-jax-gpu-nojit', 'GPU, no-JIT'),
+ ('CVMatrix-jax-gpu-coldjit', 'GPU, cold-JIT'),
+ ('CVMatrix-jax-gpu-warmjit', 'GPU, warm-JIT'),
+ ('CVMatrix-jax-cpu-nojit', 'CPU, no-JIT'),
+ ('CVMatrix-jax-cpu-coldjit', 'CPU, cold-JIT'),
+ ('CVMatrix-jax-cpu-warmjit', 'CPU, warm-JIT'),
+ ]
+ cm = colormaps.get_cmap('tab10')
+ variant_colors = {m: cm(i) for i, (m, _lab) in enumerate(variants)}
+ N = df['N'].unique()[0]
+ K = df['K'].unique()[0]
+ M = df['M'].unique()[0]
+ Ps_all = sorted(df['P'].unique())
+ ymax = 0.0
+ for key, linestyle, _combo_lab in _COMBOS:
+ for model, _lab in variants:
+ Ps, times = _series_times(df, model, key, Ps_all)
+ if times:
+ ax.plot(Ps, times, color=variant_colors[model], linestyle=linestyle,
+ linewidth=1.8)
+ ymax = max(ymax, max(times))
+ _add_time_reflines(ax, ymax)
+ ax.set_xscale('log')
+ ax.set_yscale('log')
+ ax.set_title('CVMatrix JAX backend: execution-mode comparison\n'
+ f'(weighted, N={N:,}, K={K}, M={M})', fontsize=18)
+ ax.set_xlabel('P (cross-validation folds)')
+ ax.set_ylabel('Time (s)')
+ variant_handles = [Line2D([0], [0], color=variant_colors[m], lw=2.5, label=lab)
+ for m, lab in variants]
+ combo_handles = [Line2D([0], [0], color='gray', linestyle=ls, label=lab)
+ for _k, ls, lab in _COMBOS]
+ leg1 = ax.legend(handles=variant_handles, loc='upper left', title='JAX mode / device')
+ ax.add_artist(leg1)
+ ax.legend(handles=combo_handles, loc='lower right', title='Preprocessing')
+ fig.text(0.5, 0.01, 'cvmatrix 3.2.0; multi-threaded CPU; GPU: RTX 3090 Ti',
+ ha='center', va='bottom', fontsize=9, alpha=0.7)
+ plt.tight_layout(rect=(0, 0.03, 1, 1))
+ plt.savefig('benchmark_jax_variants.png')
+ plt.clf()
+
+
def get_combination_to_color_map():
preprocessing_combinations = []
center_Xs = [False, True]
@@ -223,5 +396,12 @@ def get_combination_to_color_map():
plt.rcParams.update({'font.size': 12})
df = pd.read_csv('benchmark_results.csv')
combination_to_color_map = get_combination_to_color_map()
- plot_cvmatrix_vs_naive(df, combination_to_color_map)
- plot_cvmatrix(df, combination_to_color_map)
+ for weights in [True, False]:
+ plot_cvmatrix_vs_naive(df, combination_to_color_map, weights)
+ plot_cvmatrix(df, combination_to_color_map, weights)
+ # Multi-threaded CVMatrix NumPy-vs-JAX and JAX execution-mode figures, from the
+ # separate results CSV produced by the BENCH_JAX_VARIANTS / multi-threaded runs.
+ if os.path.exists('benchmark_results_jax.csv'):
+ df_jax = pd.read_csv('benchmark_results_jax.csv')
+ plot_numpy_vs_jax(df_jax, combination_to_color_map)
+ plot_jax_variants(df_jax)
diff --git a/cvmatrix/__init__.py b/cvmatrix/__init__.py
index 0e2a5c9..9e40323 100644
--- a/cvmatrix/__init__.py
+++ b/cvmatrix/__init__.py
@@ -1,4 +1,4 @@
-__version__ = "3.1.6"
+__version__ = "3.2.0"
__all__ = ["CVMatrix", "Partitioner"]
from .cvmatrix import CVMatrix
from .partitioner import Partitioner
diff --git a/cvmatrix/cvmatrix.py b/cvmatrix/cvmatrix.py
index 3fba937..ee0135d 100644
--- a/cvmatrix/cvmatrix.py
+++ b/cvmatrix/cvmatrix.py
@@ -10,11 +10,84 @@
E-mail: ocge@foss.dk
"""
-from typing import Optional, Tuple, Union
+from typing import TYPE_CHECKING, Literal, Optional, Tuple, Union
import numpy as np
from numpy import typing as npt
+try:
+ # Broaden the array/scalar type hints so that, with backend="jax", jax.numpy values
+ # (which are jax.Array instances, including under jit/vmap tracing) satisfy runtime
+ # type checking. numpy remains the only required dependency; this is skipped if jax
+ # is not installed, leaving the hints numpy-only.
+ import jax as _jax
+
+ Array = Union[np.ndarray, _jax.Array]
+ Scalar = Union[np.floating, _jax.Array, float, int]
+ # float dtype types accepted by `dtype`: numpy float types (`type[np.floating]`) and
+ # JAX scalar dtypes. The latter (e.g. jnp.float64/float32/bfloat16) are instances of
+ # JAX's scalar-type metaclass -- NOT numpy.floating subclasses -- so they must be
+ # admitted via that metaclass.
+ _JaxScalarMeta = type(_jax.numpy.float64)
+ FloatDType = Union[type[np.floating], _JaxScalarMeta]
+ # Abstract values produced while a function is traced by jax.jit/jax.vmap. Used to
+ # skip data-dependent validity raises that cannot run under tracing (the host-side
+ # caller is expected to validate folds before vmap); eager (concrete) jax values are
+ # NOT tracers, so they still validate like the numpy backend.
+ _TRACER_TYPES: tuple = (_jax.core.Tracer,)
+except ImportError: # pragma: no cover - exercised only without jax
+ Array = np.ndarray
+ Scalar = Union[np.floating, float, int]
+ FloatDType = type[np.floating]
+ _TRACER_TYPES = ()
+
+if TYPE_CHECKING:
+ # JAX's scalar-dtype metaclass (`type(jnp.float64)`) has no public *static* type, so
+ # the precise runtime `FloatDType` union above is built from a value (`_JaxScalarMeta`)
+ # that static type checkers reject in a type expression ("variables are not allowed in
+ # type expressions"). For static analysis only, expose numpy's standard dtype-specifier
+ # alias, which cleanly accepts both numpy float types and JAX scalar dtypes. At run
+ # time `TYPE_CHECKING` is False, so typeguard still validates against the precise union.
+ FloatDType = npt.DTypeLike
+
+
+def _resolve_backend(backend: str):
+ """
+ Resolve the array-namespace module for a given backend name.
+
+ Parameters
+ ----------
+ backend : {"numpy", "jax"}
+ The array backend to use. ``"numpy"`` uses ``numpy`` (the default and only
+ required dependency). ``"jax"`` uses ``jax.numpy`` so that the per-fold training
+ matrix computations can run inside ``jax.jit``/``jax.vmap`` on CPU/GPU/TPU; it
+ requires the optional ``jax`` dependency (``pip install cvmatrix[jax]``).
+
+ Returns
+ -------
+ module
+ The resolved array namespace (``numpy`` or ``jax.numpy``).
+
+ Raises
+ ------
+ ValueError
+ If ``backend`` is not ``"numpy"`` or ``"jax"``.
+ ImportError
+ If ``backend == "jax"`` but JAX is not installed.
+ """
+ if backend == "numpy":
+ return np
+ if backend == "jax":
+ try:
+ import jax.numpy as jnp
+ except ImportError as e: # pragma: no cover - exercised only without jax
+ raise ImportError(
+ "backend='jax' requires the optional JAX dependency. Install it with "
+ "`pip install cvmatrix[jax]`."
+ ) from e
+ return jnp
+ raise ValueError(f"Invalid backend: {backend!r}. Must be 'numpy' or 'jax'.")
+
class CVMatrix:
r"""
@@ -81,8 +154,9 @@ def __init__(
scale_X: bool = True,
scale_Y: bool = True,
ddof: int = 1,
- dtype: type[np.floating] = np.float64,
+ dtype: FloatDType = np.float64,
copy: bool = True,
+ backend: Literal["numpy", "jax"] = "numpy",
) -> None:
self.center_X = center_X
self.center_Y = center_Y
@@ -91,6 +165,18 @@ def __init__(
self.ddof = ddof
self.dtype = dtype.type if isinstance(dtype, np.dtype) else dtype
self.copy = copy
+ self.backend = backend
+ # Array namespace: numpy (default) or jax.numpy. All array operations are routed
+ # through `self.xp` so that, with backend="jax", the per-fold `training_*`
+ # methods can be traced by jax.jit/jax.vmap. The numpy backend is byte-identical
+ # to previous releases.
+ self.xp = _resolve_backend(backend)
+ if backend == "jax" and np.dtype(self.dtype).itemsize >= 8:
+ # JAX defaults to 32-bit; enable 64-bit precision so a requested float64
+ # dtype is honored instead of being silently truncated to float32.
+ import jax
+
+ jax.config.update("jax_enable_x64", True)
self.resolution = np.finfo(dtype).resolution * 10
self.X = None
self.Y = None
@@ -139,10 +225,10 @@ def fit(
Attributes
----------
- X : np.ndarray
+ X : Array
The total predictor matrix `X` for the entire dataset.
- Y : np.ndarray or None
+ Y : Array or None
The total response matrix `Y` for the entire dataset. If `Y` is `None`, this is
`None`.
@@ -155,60 +241,60 @@ def fit(
M : int or None
The number of response variables in `Y`. If `Y` is `None`, this is `None`.
- XTX : np.ndarray
+ XTX : Array
The total matrix :math:`\mathbf{X}^{\mathbf{T}}\mathbf{W}\mathbf{X}` for the
entire dataset.
- XTY : np.ndarray or None
+ XTY : Array or None
The total matrix :math:`\mathbf{X}^{\mathbf{T}}\mathbf{W}\mathbf{Y}` for the
entire dataset. This is computed only if `Y` is not `None`.
- sum_X : np.ndarray or None
+ sum_X : Array or None
The row of column-wise weighted sums of `X` for the entire dataset. This is
computed only if `center_X`, `scale_X`, or `center_Y` is `True`. This is the
row of column-wise sums of :math:`\mathbf{W}\mathbf{X}` if `weights` are
provided and otherwise the row of column-wise sums of :math:`\mathbf{X}`.
- sum_Y : np.ndarray or None
+ sum_Y : Array or None
The row of column-wise weighted sums of `Y` for the entire dataset. This is
computed only if `center_Y`, `scale_Y`, or `center_X` is `True` and `Y` is not
`None`. This is the row of column-wise sums of :math:`\mathbf{W}\mathbf{Y}` if
`weights` are provided and otherwise the row of column-wise sums of
:math:`\mathbf{Y}`.
- sum_sq_X : np.ndarray or None
+ sum_sq_X : Array or None
The row of column-wise weighted squared sums of `X` for the entire dataset.
This is computed only if `scale_X` is `True`. This is the
row of column-wise sums of :math:`\mathbf{W}\mathbf{X}\odot\mathbf{X}` if
`weights` are provided and otherwise the row of column-wise sums of
:math:`\mathbf{X}\odot\mathbf{X}`.
- sum_sq_Y : np.ndarray or None
+ sum_sq_Y : Array or None
The row of column-wise weighted squared sums of `Y` for the entire dataset.
This is computed only if `scale_Y` is `True` and `Y` is not `None`. This is the
row of column-wise sums of :math:`\mathbf{W}\mathbf{Y}\odot\mathbf{Y}` if
`weights` are provided and otherwise the row of column-wise sums of
:math:`\mathbf{Y}\odot\mathbf{Y}`.
- sq_X : np.ndarray or None
+ sq_X : Array or None
The total weighted squared predictor matrix `X` for the entire dataset. This is
:math:`\mathbf{W}\mathbf{X}\odot\mathbf{X}`. This is computed only if
`scale_X` is `True`.
- sq_Y : np.ndarray or None
+ sq_Y : Array or None
The total weighted squared response matrix `Y` for the entire dataset. This is
:math:`\mathbf{W}\mathbf{Y}\odot\mathbf{Y}`. This is computed only if
`scale_Y` is `True` and `Y` is not `None`.
- WX : np.ndarray or None
+ WX : Array or None
The total weighted predictor matrix `X` for the entire dataset. This is
:math:`\mathbf{W}\mathbf{X}`.
- WY : np.ndarray or None
+ WY : Array or None
The total weighted response matrix `Y` for the entire dataset. This is
:math:`\mathbf{W}\mathbf{Y}`. This is computed only if `Y` is not `None`.
- weights : np.ndarray or None
+ weights : Array or None
The total weights for the entire dataset. This is an array of shape (N, 1). If
`weights` is `None`, this is `None`.
@@ -237,7 +323,7 @@ def fit(
def training_XTX(
self, validation_indices: npt.NDArray[np.int_]
) -> Tuple[
- np.ndarray, Tuple[Optional[np.ndarray], Optional[np.ndarray], None, None]
+ Array, Tuple[Optional[Array], Optional[Array], None, None]
]:
r"""
Computes the training set :math:`\mathbf{X}^{\mathbf{T}}\mathbf{W}\mathbf{X}`
@@ -292,12 +378,12 @@ def training_XTX(
def training_XTY(
self, validation_indices: npt.NDArray[np.int_]
) -> Tuple[
- np.ndarray,
+ Array,
Tuple[
- Optional[np.ndarray],
- Optional[np.ndarray],
- Optional[np.ndarray],
- Optional[np.ndarray],
+ Optional[Array],
+ Optional[Array],
+ Optional[Array],
+ Optional[Array],
],
]:
r"""
@@ -358,12 +444,12 @@ def training_XTY(
def training_XTX_XTY(
self, validation_indices: npt.NDArray[np.int_]
) -> Tuple[
- Tuple[np.ndarray, np.ndarray],
+ Tuple[Array, Array],
Tuple[
- Optional[np.ndarray],
- Optional[np.ndarray],
- Optional[np.ndarray],
- Optional[np.ndarray],
+ Optional[Array],
+ Optional[Array],
+ Optional[Array],
+ Optional[Array],
],
]:
r"""
@@ -426,10 +512,10 @@ def training_XTX_XTY(
def training_statistics(
self, validation_indices: npt.NDArray[np.int_]
) -> Tuple[
- Optional[np.ndarray],
- Optional[np.ndarray],
- Optional[np.ndarray],
- Optional[np.ndarray],
+ Optional[Array],
+ Optional[Array],
+ Optional[Array],
+ Optional[Array],
]:
"""
Computes the row of column-wise weighted means and standard deviations for `X`
@@ -448,7 +534,7 @@ def training_statistics(
Returns
-------
- Tuple of four elements of Optional[np.ndarray]
+ Tuple of four elements of Optional[Array]
A tuple containing the row of column-wise weighted means for `X`, the row
of column-wise weighted standard deviations for `X`, the row of column-wise
weighted means for `Y`, and the row of column-wise weighted standard
@@ -480,9 +566,22 @@ def training_statistics(
return_Y_std=self.scale_Y and self.Y is not None,
)[:-1] # Exclude the sum of training weights from the return tuple
+ def _as_scalar(self, x):
+ """
+ Cast a scalar-valued result to the configured dtype.
+
+ For the numpy backend this returns a NumPy scalar (preserving the historical
+ ``np.floating`` return types and byte-identical behavior). For the jax backend
+ it returns the value unchanged (already a ``jax.numpy`` value of the right
+ dtype), keeping the per-fold path trace-safe.
+ """
+ if self.backend == "numpy":
+ return self.dtype(x)
+ return x
+
def _get_sum_w_train_and_num_nonzero_w_train(
self, val_indices: npt.NDArray[np.int_]
- ) -> Tuple[np.floating, np.floating]:
+ ) -> Tuple[Scalar, Scalar]:
"""
Returns a tuple containing the sum of weights in the training set and the number
of non-zero weights in the training set. If `self.weights` is `None`, it returns
@@ -502,16 +601,21 @@ def _get_sum_w_train_and_num_nonzero_w_train(
make it impossible to compute either of training set means or standard
deviations.
"""
+ xp = self.xp
if self.weights is None:
sum_w_val = val_indices.size
- sum_w_train = self.dtype(self.sum_w - sum_w_val)
+ sum_w_train = self._as_scalar(self.sum_w - sum_w_val)
return (sum_w_train, sum_w_train)
w_val = self.weights[val_indices]
- sum_w_val = np.sum(w_val)
- sum_w_train = self.dtype(self.sum_w - sum_w_val)
- num_nonzero_w_val = np.count_nonzero(w_val)
- num_nonzero_w_train = self.dtype(self.num_nonzero_w - num_nonzero_w_val)
- if num_nonzero_w_train == 0:
+ sum_w_val = xp.sum(w_val)
+ sum_w_train = self._as_scalar(self.sum_w - sum_w_val)
+ num_nonzero_w_val = xp.count_nonzero(w_val)
+ num_nonzero_w_train = self._as_scalar(self.num_nonzero_w - num_nonzero_w_val)
+ # Validate eagerly (numpy, or concrete/eager jax). Under jax.jit/vmap tracing the
+ # input `val_indices` is a tracer (in both the weighted and unweighted paths), so
+ # we skip the data-dependent raise; degenerate folds are then expected to be
+ # rejected by a host-side pre-flight before vmap.
+ if not isinstance(val_indices, _TRACER_TYPES) and num_nonzero_w_train == 0:
raise ValueError(
"The number of non-zero weights in the training set must be "
"greater than zero."
@@ -521,18 +625,18 @@ def _get_sum_w_train_and_num_nonzero_w_train(
def _compute_training_stats(
self,
val_indices: npt.NDArray[np.int_],
- X_val: Optional[np.ndarray],
- Y_val: Optional[np.ndarray],
+ X_val: Optional[Array],
+ Y_val: Optional[Array],
return_X_mean: bool,
return_X_std: bool,
return_Y_mean: bool,
return_Y_std: bool,
) -> Tuple[
- Optional[np.ndarray],
- Optional[np.ndarray],
- Optional[np.ndarray],
- Optional[np.ndarray],
- Optional[np.floating],
+ Optional[Array],
+ Optional[Array],
+ Optional[Array],
+ Optional[Array],
+ Optional[Scalar],
]:
"""
Computes the training set statistics. The training set corresponds
@@ -572,7 +676,7 @@ def _compute_training_stats(
Returns
-------
- Tuple of Optional[np.ndarray]
+ Tuple of Optional[Array]
A tuple containing the row of column-wise weighted means for `X`, the row
of column-wise weighted standard deviations for `X`, the row of column-wise
weighted means for `Y`, the row of column-wise weighted standard deviations
@@ -586,27 +690,34 @@ def _compute_training_stats(
and not return_Y_std
):
return None, None, None, None, None
+ # Under jax.jit/vmap, `val_indices` is a tracer; data-dependent validity raises
+ # are then deferred to a host-side pre-flight (see `_get_sum_w_train_*` and
+ # `_compute_std_divisor`). The unweighted path derives its counts from static
+ # shapes, so it cannot rely on the count itself being a tracer.
+ traced = isinstance(val_indices, _TRACER_TYPES)
sum_w_train, num_nonzero_w_train = (
self._get_sum_w_train_and_num_nonzero_w_train(val_indices)
)
if return_X_mean or return_X_std:
- sum_X_val = np.sum(X_val, axis=0, keepdims=True)
+ sum_X_val = self.xp.sum(X_val, axis=0, keepdims=True)
sum_X_train = self._compute_train_mat_sum(sum_X_val, self.sum_X)
X_train_mean = self._compute_training_mat_mean(
sum_X_train,
sum_w_train,
)
if return_Y_mean or return_Y_std:
- sum_Y_val = np.sum(Y_val, axis=0, keepdims=True)
+ sum_Y_val = self.xp.sum(Y_val, axis=0, keepdims=True)
sum_Y_train = self._compute_train_mat_sum(sum_Y_val, self.sum_Y)
Y_train_mean = self._compute_training_mat_mean(
sum_Y_train,
sum_w_train,
)
if return_X_std or return_Y_std:
- divisor = self._compute_std_divisor(sum_w_train, num_nonzero_w_train)
+ divisor = self._compute_std_divisor(
+ sum_w_train, num_nonzero_w_train, traced
+ )
if return_X_std:
- sum_sq_X_val = np.sum(self.sq_X[val_indices], axis=0, keepdims=True)
+ sum_sq_X_val = self.xp.sum(self.sq_X[val_indices], axis=0, keepdims=True)
sum_sq_X_train = self._compute_train_mat_sum(sum_sq_X_val, self.sum_sq_X)
X_train_std = self._compute_training_mat_std(
sum_sq_X_train,
@@ -616,7 +727,7 @@ def _compute_training_stats(
divisor,
)
if return_Y_std:
- sum_sq_Y_val = np.sum(self.sq_Y[val_indices], axis=0, keepdims=True)
+ sum_sq_Y_val = self.xp.sum(self.sq_Y[val_indices], axis=0, keepdims=True)
sum_sq_Y_train = self._compute_train_mat_sum(sum_sq_Y_val, self.sum_sq_Y)
Y_train_std = self._compute_training_mat_std(
sum_sq_Y_train,
@@ -636,12 +747,12 @@ def _compute_training_stats(
def _training_matrices(
self, return_XTX: bool, return_XTY: bool, val_indices: npt.NDArray[np.int_]
) -> Tuple[
- Union[np.ndarray, Tuple[np.ndarray, np.ndarray]],
+ Union[Array, Tuple[Array, Array]],
Tuple[
- Optional[np.ndarray],
- Optional[np.ndarray],
- Optional[np.ndarray],
- Optional[np.ndarray],
+ Optional[Array],
+ Optional[Array],
+ Optional[Array],
+ Optional[Array],
],
]:
r"""
@@ -780,10 +891,10 @@ def _training_matrices(
def _get_val_matrices(
self, val_indices: npt.NDArray[np.int_], return_XTY: bool
) -> Tuple[
- np.ndarray,
- np.ndarray,
- Optional[np.ndarray],
- Optional[np.ndarray],
+ Array,
+ Array,
+ Optional[Array],
+ Optional[Array],
]:
"""
Returns the validation set matrices for a given fold.
@@ -824,16 +935,16 @@ def _get_val_matrices(
def _training_kernel_matrix(
self,
- total_kernel_mat: np.ndarray,
- X_val: np.ndarray,
- mat2_val: np.ndarray,
- X_train_mean: Optional[np.ndarray] = None,
- mat2_train_mean: Optional[np.ndarray] = None,
- X_train_std: Optional[np.ndarray] = None,
- mat2_train_std: Optional[np.ndarray] = None,
- sum_w_train: Optional[np.floating] = None,
+ total_kernel_mat: Array,
+ X_val: Array,
+ mat2_val: Array,
+ X_train_mean: Optional[Array] = None,
+ mat2_train_mean: Optional[Array] = None,
+ X_train_std: Optional[Array] = None,
+ mat2_train_std: Optional[Array] = None,
+ sum_w_train: Optional[Scalar] = None,
center: bool = False,
- ) -> np.ndarray:
+ ) -> Array:
r"""
Computes the training set kernel matrix for a given fold.
@@ -893,9 +1004,9 @@ def _training_kernel_matrix(
def _compute_train_mat_sum(
self,
- sum_mat_val: np.ndarray,
- sum_mat: np.ndarray,
- ) -> np.ndarray:
+ sum_mat_val: Array,
+ sum_mat: Array,
+ ) -> Array:
"""
Computes the row vector of column-wise sums of a matrix for a given fold.
"""
@@ -903,9 +1014,9 @@ def _compute_train_mat_sum(
def _compute_training_mat_mean(
self,
- sum_mat_train: np.ndarray,
- sum_w_train: np.floating,
- ) -> np.ndarray:
+ sum_mat_train: Array,
+ sum_w_train: Scalar,
+ ) -> Array:
"""
Computes the row of column-wise means of a matrix for a given fold.
@@ -925,8 +1036,8 @@ def _compute_training_mat_mean(
return sum_mat_train / sum_w_train
def _compute_std_divisor(
- self, sum_w_train: np.floating, num_nonzero_w_train: np.floating
- ) -> np.floating:
+ self, sum_w_train: Scalar, num_nonzero_w_train: Scalar, traced: bool = False
+ ) -> Scalar:
"""
Computes the divisor for the standard deviation calculation based on the number
of samples in the training set and the number of non-zero weights.
@@ -939,12 +1050,21 @@ def _compute_std_divisor(
num_nonzero_w_train : int
The number of non-zero weights in the training set.
+ traced : bool, default=False
+ Whether the computation is being traced by jax.jit/jax.vmap. When True, the
+ data-dependent degenerate-fold check is skipped (deferred to a host-side
+ pre-flight); a non-positive divisor then degrades gracefully via the clamp in
+ `_compute_training_mat_std`.
+
Returns
-------
float
The divisor for the standard deviation calculation.
"""
- if num_nonzero_w_train <= self.ddof:
+ # Validate eagerly (numpy, or concrete/eager jax). Under jax.jit/vmap tracing
+ # (`traced`, detected from the input `val_indices`) the check is deferred to a
+ # host-side pre-flight before vmap.
+ if not traced and num_nonzero_w_train <= self.ddof:
raise ValueError(
"The number of non-zero weights in the training set must be greater "
"than `ddof`."
@@ -953,12 +1073,12 @@ def _compute_std_divisor(
def _compute_training_mat_std(
self,
- sum_sq_mat_train: np.ndarray,
- mat_train_mean: np.ndarray,
- sum_mat_train: np.ndarray,
- sum_w_train: np.floating,
- divisor: np.floating,
- ) -> np.ndarray:
+ sum_sq_mat_train: Array,
+ mat_train_mean: Array,
+ sum_mat_train: Array,
+ sum_w_train: Scalar,
+ divisor: Scalar,
+ ) -> Array:
r"""
Computes the row of column-wise standard deviations of a matrix for a given
fold.
@@ -988,17 +1108,20 @@ def _compute_training_mat_std(
Array of shape (1, K) or (1, M)
The row of column-wise standard deviations of the training set matrix.
"""
+ xp = self.xp
mat_train_var = (
-2 * mat_train_mean * sum_mat_train
+ sum_w_train * mat_train_mean**2
+ sum_sq_mat_train
) / divisor
- mat_train_var[mat_train_var < 0] = 0
- mat_train_std = np.sqrt(mat_train_var)
- mat_train_std[mat_train_std <= self.resolution] = 1
+ # `xp.maximum`/`xp.where` instead of boolean-mask in-place assignment: identical
+ # results for numpy and trace-safe for jax (jit/vmap).
+ mat_train_var = xp.maximum(mat_train_var, 0)
+ mat_train_std = xp.sqrt(mat_train_var)
+ mat_train_std = xp.where(mat_train_std <= self.resolution, 1, mat_train_std)
return mat_train_std
- def _init_mat(self, mat: np.ndarray) -> np.ndarray:
+ def _init_mat(self, mat: Array) -> Array:
"""
Casts the matrix to the dtype specified in the constructor and reshapes it if
the matrix is one-dimensional.
@@ -1013,7 +1136,7 @@ def _init_mat(self, mat: np.ndarray) -> np.ndarray:
Array of shape (N, K) or (N, M) or (N, 1)
The initialized matrix.
"""
- mat = np.asarray(mat, dtype=self.dtype)
+ mat = self.xp.asarray(mat, dtype=self.dtype)
if self.copy and mat.dtype == self.dtype:
mat = mat.copy()
if mat.ndim == 1:
@@ -1055,7 +1178,7 @@ def _init_mats(
if weights is not None:
self.weights = self._init_mat(weights)
- if np.any(self.weights < 0):
+ if bool(self.xp.any(self.weights < 0)):
raise ValueError("Weights must be non-negative.")
else:
self.weights = None
@@ -1092,22 +1215,22 @@ def _init_stats(self) -> None:
"""
if self.center_X or self.center_Y or self.scale_X or self.scale_Y:
if self.weights is not None:
- self.sum_w = np.sum(self.weights)
- self.num_nonzero_w = np.count_nonzero(self.weights)
+ self.sum_w = self.xp.sum(self.weights)
+ self.num_nonzero_w = self.xp.count_nonzero(self.weights)
else:
self.sum_w = self.N
self.num_nonzero_w = self.N
if self.center_X or self.center_Y or self.scale_X:
- self.sum_X = np.sum(self.WX, axis=0, keepdims=True)
+ self.sum_X = self.xp.sum(self.WX, axis=0, keepdims=True)
if (self.center_X or self.center_Y or self.scale_Y) and self.Y is not None:
- self.sum_Y = np.sum(self.WY, axis=0, keepdims=True)
+ self.sum_Y = self.xp.sum(self.WY, axis=0, keepdims=True)
if self.scale_X:
self.sq_X = self.WX * self.X
- self.sum_sq_X = np.sum(self.sq_X, axis=0, keepdims=True)
+ self.sum_sq_X = self.xp.sum(self.sq_X, axis=0, keepdims=True)
else:
self.sum_sq_X = None
if self.scale_Y and self.Y is not None:
self.sq_Y = self.WY * self.Y
- self.sum_sq_Y = np.sum(self.sq_Y, axis=0, keepdims=True)
+ self.sum_sq_Y = self.xp.sum(self.sq_Y, axis=0, keepdims=True)
else:
self.sum_sq_Y = None
diff --git a/examples/training_matrices_jax.py b/examples/training_matrices_jax.py
new file mode 100644
index 0000000..e9110d8
--- /dev/null
+++ b/examples/training_matrices_jax.py
@@ -0,0 +1,78 @@
+r"""
+This file demonstrates the optional **JAX backend** of CVMatrix (``backend="jax"``).
+With the JAX backend, the per-fold training matrices
+:math:`\mathbf{X}^{\mathbf{T}}\mathbf{W}\mathbf{X}` and
+:math:`\mathbf{X}^{\mathbf{T}}\mathbf{W}\mathbf{Y}` (with training-set centering/scaling
+and weighting) are computed with ``jax.numpy``. Because each ``training_*`` call is a
+pure function of the (closed-over) dataset-wide statistics and the validation indices,
+it can be traced by ``jax.jit`` and -- most usefully -- batched over folds with
+``jax.vmap`` to run all folds together on a CPU/GPU/TPU.
+
+The numerical results are identical to the default ``backend="numpy"``.
+
+Note: requires the optional JAX dependency, installed with ``pip install cvmatrix[jax]``.
+
+Author: Ole-Christian Galbo Engstrøm
+E-mail: ocge@foss.dk
+"""
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+
+from cvmatrix import CVMatrix, Partitioner
+
+# Honor float64 (JAX defaults to float32). CVMatrix also enables this automatically when
+# constructed with backend="jax" and a 64-bit dtype.
+jax.config.update("jax_enable_x64", True)
+
+
+if __name__ == "__main__":
+ rng = np.random.default_rng(42)
+ N, K, M = 100, 10, 3
+ X = rng.uniform(size=(N, K))
+ Y = rng.uniform(size=(N, M))
+ weights = rng.uniform(size=(N,)) + 0.1 # non-negative
+
+ # 5-fold cross-validation (equal-size folds -> a single vmap shape).
+ folds = np.arange(N) % 5
+
+ # Build a JAX-backed CVMatrix. The only change from the numpy backend is
+ # backend="jax"; the API is identical.
+ cvm = CVMatrix(
+ center_X=True, center_Y=True, scale_X=True, scale_Y=True, backend="jax"
+ )
+ cvm.fit(X, Y, weights)
+
+ # Partitioner does host-side fold/index bookkeeping (numpy). Stack the per-fold
+ # validation indices into a single (n_folds, fold_size) array and move it to the
+ # device once; jax.vmap then computes every fold's training matrices together.
+ p = Partitioner(folds)
+ val_index_batch = jnp.asarray(
+ np.stack([p.get_validation_indices(fold) for fold in p.folds_dict])
+ )
+
+ # vmap (optionally under jit) the per-fold training-matrix computation over folds.
+ batched_training_XTX_XTY = jax.jit(jax.vmap(cvm.training_XTX_XTY))
+ (XTWX, XTWY), (X_mean, X_std, Y_mean, Y_std) = batched_training_XTX_XTY(
+ val_index_batch
+ )
+
+ # The leading axis of each output indexes the folds.
+ print(f"Folds: {list(p.folds_dict.keys())}")
+ print(f"Batched training XTWX shape: {XTWX.shape} (n_folds, K, K)")
+ print(f"Batched training XTWY shape: {XTWY.shape} (n_folds, K, M)")
+ print(f"Batched training X mean shape: {X_mean.shape} (n_folds, 1, K)")
+
+ # Cross-check the first fold against the numpy backend (identical results).
+ cvm_np = CVMatrix(
+ center_X=True, center_Y=True, scale_X=True, scale_Y=True, backend="numpy"
+ )
+ cvm_np.fit(X, Y, weights)
+ first_fold = next(iter(p.folds_dict))
+ (XTWX_np, XTWY_np), _ = cvm_np.training_XTX_XTY(p.get_validation_indices(first_fold))
+ print(
+ "Max |jax - numpy| for fold "
+ f"{first_fold!r}: XTWX={np.max(np.abs(np.asarray(XTWX[0]) - XTWX_np)):.2e}, "
+ f"XTWY={np.max(np.abs(np.asarray(XTWY[0]) - XTWY_np)):.2e}"
+ )
diff --git a/poetry.lock b/poetry.lock
deleted file mode 100644
index 6054a26..0000000
--- a/poetry.lock
+++ /dev/null
@@ -1,87 +0,0 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
-
-[[package]]
-name = "numpy"
-version = "2.4.0"
-description = "Fundamental package for array computing in Python"
-optional = false
-python-versions = ">=3.11"
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- {file = "numpy-2.4.0.tar.gz", hash = "sha256:6e504f7b16118198f138ef31ba24d985b124c2c469fe8467007cf30fd992f934"},
-]
-
-[metadata]
-lock-version = "2.0"
-python-versions = ">=3.11, <3.15"
-content-hash = "cebd365999c19877d1f150434d37f379d33e6b0becd4c88c452ce71dfdf124d1"
diff --git a/pyproject.toml b/pyproject.toml
index cc49a10..72e57ba 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,27 +1,47 @@
-[tool.poetry]
+[project]
name = "cvmatrix"
-version = "3.1.6"
+version = "3.2.0"
description = "Fast computation of possibly weighted and possibly centered/scaled training set kernel matrices in a cross-validation setting."
-authors = ["Sm00thix "]
-maintainers = ["Sm00thix "]
+authors = [{ name = "Sm00thix", email = "oleemail@icloud.com" }]
+maintainers = [{ name = "Sm00thix", email = "oleemail@icloud.com" }]
license = "Apache-2.0"
readme = "README.md"
-homepage = "https://cvmatrix.readthedocs.io/en/latest/"
-repository = "https://github.com/Sm00thix/CVMatrix"
+requires-python = ">=3.11, <3.15"
classifiers = [
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
]
+dependencies = [
+ "numpy>=2.0.0,<3.0.0",
+]
+
+[project.optional-dependencies]
+# Optional JAX backend (backend="jax"): enables tracing the per-fold training-matrix
+# computations under jax.jit/jax.vmap on CPU/GPU/TPU. Install with `cvmatrix[jax]`.
+jax = ["jax>=0.4.0"]
+
+[project.urls]
+Homepage = "https://cvmatrix.readthedocs.io/en/latest/"
+Repository = "https://github.com/Sm00thix/CVMatrix"
-[tool.poetry.dependencies]
-python = ">=3.11, <3.15"
-numpy = "^2.0.0"
+# Development/test-only dependencies (PEP 735). Installed by `uv sync`.
+[dependency-groups]
+dev = [
+ "pandas",
+ "pytest",
+ "pytest-cov",
+ "typeguard",
+ "ruff",
+]
[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["cvmatrix"]
[tool.pytest.ini_options]
addopts = [
@@ -30,4 +50,4 @@ addopts = [
"--cov-report=xml",
"--cov-report=html",
"--cov-report=term-missing",
-]
\ No newline at end of file
+]
diff --git a/tests/test_cvmatrix.py b/tests/test_cvmatrix.py
index 5cdc929..3e067a6 100644
--- a/tests/test_cvmatrix.py
+++ b/tests/test_cvmatrix.py
@@ -59,6 +59,18 @@ class tests for equivalency between the naive, straight-forward algorithms
# ones_weights = ones_weights[indices]
# random_weights = random_weights[indices]
+ @pytest.fixture(params=["numpy", "jax"], autouse=True)
+ def _set_backend(self, request):
+ """
+ Run every test with both the "numpy" and "jax" CVMatrix backends. The jax
+ parametrization is skipped if JAX is not installed. `fit_fast` builds the
+ CVMatrix model with `backend=self.backend`, so all equivalence and behavior
+ tests exercise both backends.
+ """
+ if request.param == "jax":
+ pytest.importorskip("jax")
+ self.backend = request.param
+
def load_X(self) -> npt.NDArray[np.float64]:
"""
Loads the raw spectral data.
@@ -286,7 +298,10 @@ def fit_fast(
made. If `True` a copy is always made. If no copy is made, then external
modifications to `X` or `Y` will result in undefined behavior.
"""
- fast = CVMatrix(center_X, center_Y, scale_X, scale_Y, ddof, dtype, copy)
+ fast = CVMatrix(
+ center_X, center_Y, scale_X, scale_Y, ddof, dtype, copy,
+ backend=getattr(self, "backend", "numpy"),
+ )
fast.fit(X, Y, weights)
return fast
@@ -559,6 +574,114 @@ def test_all_preprocessing_combinations(self):
)
self.check_equivalent_matrices(naive, fast, p)
+ def test_jax_backend_requires_jax(self, monkeypatch):
+ """
+ Tests that requesting backend="jax" raises ImportError with an install hint when
+ JAX is not installed. JAX absence is simulated by hiding it from `sys.modules`,
+ so this test is valid regardless of whether JAX is installed in the environment.
+ """
+ monkeypatch.setitem(sys.modules, "jax", None)
+ monkeypatch.setitem(sys.modules, "jax.numpy", None)
+ with pytest.raises(ImportError, match="cvmatrix\\[jax\\]"):
+ CVMatrix(backend="jax")
+
+ def test_invalid_backend(self):
+ """
+ Tests that an unknown backend string raises ValueError (the sibling of the
+ ImportError path tested in `test_jax_backend_requires_jax`). Exercised via
+ `_resolve_backend` directly: the constructor's `Literal["numpy", "jax"]` hint is
+ intercepted by typeguard before the ValueError is reached, whereas
+ `_resolve_backend(backend: str)` accepts the str and reaches the real check.
+ """
+ from cvmatrix.cvmatrix import _resolve_backend
+
+ with pytest.raises(ValueError, match="Invalid backend"):
+ _resolve_backend("torch")
+
+ def test_jax_jit_vmap_matches_numpy(self):
+ """
+ Exercises the JAX backend under jax.jit + jax.vmap (the headline feature and the
+ trace-safety machinery: the deferred degenerate-fold checks and the xp.maximum/
+ xp.where standard-deviation clamp all run under tracing here). Batches the per-
+ fold training_XTX_XTY over equal-size folds with jax.vmap and checks the results
+ match the eager numpy backend, for both unweighted and weighted folds. The
+ unweighted case specifically covers the path where the training-set count is a
+ static int rather than a tracer.
+ """
+ if getattr(self, "backend", "numpy") != "jax":
+ pytest.skip("JAX-specific trace test; runs under the jax parametrization.")
+ pytest.importorskip("jax")
+ import jax
+ import jax.numpy as jnp
+
+ X = self.load_X()[:, :20]
+ Y = self.load_Y(["Protein", "Moisture"])
+ # Equal-size folds so jax.vmap sees a fixed (n_folds, fold_size) index batch.
+ n_folds = 5
+ n = (X.shape[0] // n_folds) * n_folds
+ X, Y = X[:n], Y[:n]
+ folds = np.arange(n) % n_folds
+ p = Partitioner(folds)
+ val_index_batch = jnp.asarray(
+ np.stack([p.get_validation_indices(f) for f in p.folds_dict])
+ )
+
+ for weights in (None, self.load_weights(random=True)[:n]):
+ cvm_jax = CVMatrix(
+ center_X=True, center_Y=True, scale_X=True, scale_Y=True,
+ ddof=1, backend="jax",
+ )
+ cvm_jax.fit(X, Y, weights)
+ cvm_np = CVMatrix(
+ center_X=True, center_Y=True, scale_X=True, scale_Y=True,
+ ddof=1, backend="numpy",
+ )
+ cvm_np.fit(X, Y, weights)
+ (XTWX_b, XTWY_b), (Xm_b, Xs_b, Ym_b, Ys_b) = jax.jit(
+ jax.vmap(cvm_jax.training_XTX_XTY)
+ )(val_index_batch)
+ for i, fold in enumerate(p.folds_dict):
+ (XTWX, XTWY), (Xm, Xs, Ym, Ys) = cvm_np.training_XTX_XTY(
+ p.get_validation_indices(fold)
+ )
+ assert_allclose(np.asarray(XTWX_b[i]), XTWX, atol=1e-8, rtol=1e-8)
+ assert_allclose(np.asarray(XTWY_b[i]), XTWY, atol=1e-8, rtol=1e-8)
+ assert_allclose(np.asarray(Xm_b[i]), Xm, atol=1e-8, rtol=1e-8)
+ assert_allclose(np.asarray(Xs_b[i]), Xs, atol=1e-8, rtol=1e-8)
+ assert_allclose(np.asarray(Ym_b[i]), Ym, atol=1e-8, rtol=1e-8)
+ assert_allclose(np.asarray(Ys_b[i]), Ys, atol=1e-8, rtol=1e-8)
+
+ def test_jax_trace_defers_degenerate_check(self):
+ """
+ Under jax.jit/jax.vmap, the data-dependent degenerate-fold ValueError must be
+ skipped (deferred to a host-side pre-flight); eager execution must still raise.
+ This guards the UNWEIGHTED path specifically, where the training-set count is a
+ static int (not a tracer) and so the skip must key on the input `val_indices`
+ being a tracer rather than on the count.
+ """
+ if getattr(self, "backend", "numpy") != "jax":
+ pytest.skip("JAX-specific trace test; runs under the jax parametrization.")
+ pytest.importorskip("jax")
+ import jax
+ import jax.numpy as jnp
+
+ X = self.load_X()[:6, :4]
+ Y = self.load_Y(["Protein"])[:6]
+ cvm = CVMatrix(
+ center_X=True, center_Y=True, scale_X=True, scale_Y=True,
+ ddof=1, backend="jax",
+ )
+ cvm.fit(X, Y, None) # unweighted
+ # A degenerate fold: validating on 5 of 6 rows leaves 1 training sample (<= ddof).
+ degenerate_val = np.arange(5)
+ # Eager (concrete) execution validates and raises.
+ with pytest.raises(ValueError, match="greater than `ddof`"):
+ cvm.training_XTX_XTY(degenerate_val)
+ # Under jit, val_indices is a tracer: the check is deferred, so tracing must
+ # complete without raising (the degenerate std degrades via the clamp).
+ (XTWX, _), _ = jax.jit(cvm.training_XTX_XTY)(jnp.asarray(degenerate_val))
+ assert XTWX.shape == (4, 4)
+
def test_naive_hadamard_vs_matmul(self):
"""
Tests if the NaiveCVMatrix model gives the same result when using Hadamard
@@ -1026,6 +1149,11 @@ def test_dtype(self):
Tests that different dtypes can be used and that the output preserves the
dtype.
"""
+ if getattr(self, "backend", "numpy") == "jax":
+ pytest.skip(
+ "NumPy-specific dtype coverage (incl. float16/float128); the jax backend "
+ "supports only float32/float64."
+ )
X = np.array([1, 2, 3, 4, 5])
Y = np.array([5, 4, 3, 2, 1])
folds = np.array([0, 0, 1, 1, 2])
@@ -1080,6 +1208,11 @@ def test_copy(self):
"""
Tests that the copy parameter works as expected.
"""
+ if getattr(self, "backend", "numpy") == "jax":
+ pytest.skip(
+ "`copy`/`np.shares_memory` are NumPy-specific; the jax backend always "
+ "transfers inputs to device arrays."
+ )
dtype = np.float64
X = np.array([1, 2, 3, 4, 5]).astype(dtype)
Y = np.array([5, 4, 3, 2, 1]).astype(dtype)
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..7d0e429
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,800 @@
+version = 1
+revision = 3
+requires-python = ">=3.11, <3.15"
+resolution-markers = [
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+ "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 == '3.13.*' and sys_platform == 'win32'",
+ "python_full_version == '3.12.*' and sys_platform == 'win32'",
+ "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
+ "python_full_version == '3.12.*' and sys_platform == 'emscripten'",
+ "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+ "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+ "python_full_version < '3.12' and sys_platform == 'win32'",
+ "python_full_version < '3.12' and sys_platform == 'emscripten'",
+ "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
+[[package]]
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