diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml new file mode 100644 index 0000000..87e306a --- /dev/null +++ b/.github/workflows/lint.yaml @@ -0,0 +1,23 @@ +name: Linting + +on: + push: + branches: [main] + pull_request: + branches: [main] + +jobs: + lint: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v2 + + - name: Set up Python + uses: actions/setup-python@v5 + + - name: Run pre-commit hooks + run: uvx pre-commit run --all-files diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml new file mode 100644 index 0000000..c96f349 --- /dev/null +++ b/.github/workflows/release.yaml @@ -0,0 +1,26 @@ +name: Release +on: + push: + tags: + - "v*.*.*" + +jobs: + release: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v2 + + - name: Set up Python + uses: actions/setup-python@v5 + + - name: Build and publish + env: + TWINE_USERNAME: __token__ + TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }} + run: | + uv build + uvx twine upload dist/* diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml deleted file mode 100644 index 422d894..0000000 --- a/.github/workflows/run_tests.yml +++ /dev/null @@ -1,29 +0,0 @@ -# This workflow will install the Python package and run the tests - -name: Run tests - -on: - push: - branches: [main] - pull_request: - branches: [main] - -jobs: - build: - runs-on: ubuntu-latest - strategy: - fail-fast: false - matrix: - python-version: ["3.10", "3.11"] - - steps: - - uses: actions/checkout@v3 - - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - - name: Setup poetry - uses: abatilo/actions-poetry@v2 - - name: Install the project dependencies - run: poetry install - - name: Run the tests - run: poetry run pytest -v tests/ diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml new file mode 100644 index 0000000..0b0c877 --- /dev/null +++ b/.github/workflows/test.yaml @@ -0,0 +1,37 @@ +name: test + +on: + push: + branches: [main] + pull_request: + branches: [main] + +jobs: + build: + + runs-on: ${{ matrix.os }} + + strategy: + matrix: + os: [macos-latest, ubuntu-latest] + python-version: ["3.10", "3.11", "3.12"] + + steps: + - uses: actions/checkout@v3 + + - name: Install uv + uses: astral-sh/setup-uv@v2 + with: + enable-cache: true + cache-dependency-glob: "uv.lock" + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + + - name: Install the project + run: uv sync --all-extras --dev + + - name: Run tests + run: uv run pytest tests diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 54c962e..7e5a51a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,29 +1,33 @@ +# install pre-commit hooks with +# `pre-commit install` + +# auto-update pre-commit hooks +# `pre-commit autoupdate` + +# run pre-commit hooks on all files +# `pre-commit run --all-files` + repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 + rev: v4.6.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer - - repo: https://github.com/psf/black - rev: 23.3.0 + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.6.4 hooks: - - id: black + - id: ruff + args: [ --fix ] + - id: ruff-format - repo: https://github.com/codespell-project/codespell - rev: v2.2.2 + rev: v2.3.0 hooks: - - id: codespell - types_or: [python, markdown] - additional_dependencies: [tomli] + - id: codespell - - repo: local + - repo: https://github.com/frazane/check-latex-math + rev: v0.2.2 hooks: - - id: ruff - name: ruff - entry: ruff - args: ["--exit-non-zero-on-fix"] - require_serial: true - language: system - types: [python] - exclude: examples/ + - id: latex-math-validation + args: ["scoringrules/", "docs/", "*.py", "*.md"] diff --git a/docs/api/crps.md b/docs/api/crps.md index e8e89bb..2d11a6b 100644 --- a/docs/api/crps.md +++ b/docs/api/crps.md @@ -8,7 +8,7 @@ where $F(x) = P(X ## Analytical formulations diff --git a/docs/assets/images/banner_dark.svg b/docs/assets/images/banner_dark.svg index bafab45..1d95ca6 100644 --- a/docs/assets/images/banner_dark.svg +++ b/docs/assets/images/banner_dark.svg @@ -1,4 +1,4 @@ -
Scoringrules
ScoringrulesText is not SVG - cannot display \ No newline at end of file +
Scoringrules
ScoringrulesText is not SVG - cannot display diff --git a/docs/assets/images/banner_light.svg b/docs/assets/images/banner_light.svg index eedccc4..92e7c6c 100644 --- a/docs/assets/images/banner_light.svg +++ b/docs/assets/images/banner_light.svg @@ -1,4 +1,4 @@ -
Scoringrules
ScoringrulesText is not SVG - cannot display \ No newline at end of file +
Scoringrules
ScoringrulesText is not SVG - cannot display diff --git a/docs/contributing.md b/docs/contributing.md index 3955462..12333d6 100644 --- a/docs/contributing.md +++ b/docs/contributing.md @@ -14,22 +14,33 @@ Fork the repository on GitHub and clone it to your computer: git clone https://github.com//scoringrules.git ``` -We use [poetry](https://python-poetry.org/) for dependencies management and packaging. Once you've set it up, you can install the library and all dependencies (including development dependencies), install the pre-commit hooks and activate the environment: +We use [uv](https://docs.astral.sh/uv/) for project management and packaging. Install it with ``` -poetry install -poetry run pre-commit install -poetry shell +curl -LsSf https://astral.sh/uv/install.sh | sh ``` -From here you can work on your changes! Once you're satisfied with your changes (and followed the additional instructions below), push everything to your repository and open a pull request on GitHub. +Then, you can install the library and all dependencies (including development dependencies) and install the pre-commit hooks: + +``` +uv install +uv run pre-commit install +``` + +From here you can work on your changes! Once you're satisfied with your changes, and followed the additional instructions below, push everything to your repository and open a pull request on GitHub. ### Contributing to the codebase +Commit changes to the codebase within the project environment, so as to run pre-commit hooks: + +``` +uv run git commit -m "your commit message" +``` + Don't forget to include new tests if necessary, then make sure that all tests are passing with ``` -pytest tests/ +uv run pytest tests/ ``` ### Contributing to the documentation @@ -37,7 +48,8 @@ pytest tests/ You can work on the documentation by modifying `mkdocs.yaml` and files in `docs/`. The most convenient way to do it is to run ``` -mkdocs serve +uvx --with-requirements docs/requirements.txt mkdocs serve ``` -and open the locally hosted documentation on your browser. It will be updated automatically every time you make changes and save. +and open the locally hosted documentation on your browser. It will be updated automatically every time you make changes and save. If you edit or add pieces of LaTex math, please make sure they +are rendered correctly. diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..271dba0 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,6 @@ +mkdocs +mkdocs-bibtex +mkdocs-material +mkdocs-section-index +mkdocstrings-python +black diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 88ca2a0..0000000 --- a/poetry.lock +++ /dev/null @@ -1,4822 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. - -[[package]] -name = "absl-py" -version = "2.1.0" -description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." -optional = false -python-versions = ">=3.7" -files = [ - {file = "absl-py-2.1.0.tar.gz", hash = "sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff"}, - {file = "absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308"}, -] - 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-[[package]] -name = "zipp" -version = "3.19.2" -description = "Backport of pathlib-compatible object wrapper for zip files" -optional = false -python-versions = ">=3.8" -files = [ - {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"}, - {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"}, -] - -[package.extras] -doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] - -[metadata] -lock-version = "2.0" -python-versions = ">=3.10,<3.13" -content-hash = "2075fbf7129d68e2dae11f5fab6fb1b59db78cec08dca259080dd6669a224784" diff --git a/pyproject.toml b/pyproject.toml index 97f2846..48fb62f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,129 +1,62 @@ -[tool.poetry] +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] name = "scoringrules" version = "0.5.3" description = "Scoring rules for probabilistic forecast evaluation." +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "numpy>=1.25.0", + "scipy>=1.10.0", +] authors = [ - "Francesco Zanetta ", - "Sam Allen ", + {name = "Francesco Zanetta", email = "zanetta.francesco@gmail.com"}, + {name = "Sam Allen", email = "sam.allen@stat.math.ethz.ch"} ] -homepage = "https://github.com/frazane/scoringrules" -documentation = "https://frazane.github.io/scoringrules/" +license = {file = "LICENSE"} keywords = ["probabilistic", "forecasting", "verification"] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: MIT License", + "License :: OSI Approved :: Apache Software License", + "Topic :: Scientific/Engineering :: Mathematics", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", +] -[tool.poetry.urls] -"Homepage" = "https://github.com/frazane/scoringrules" - -[tool.poetry.dependencies] -python = ">=3.10,<3.13" -numpy = ">=1.25.0, <2.0.0" -scipy = ">=1.10.0,<2.0.0" -numba = ">=0.57.0, <1.0.0" - 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"empty-body", - "no-any-return", - "type-var", - "index", - "no-redef", +[tool.uv] +dev-dependencies = [ + "matplotlib>=3.9.2", + "pre-commit>=3.8.0", + "pytest>=8.3.2", ] -[tool.codespell] -skip = '*/functions.py' +[tool.ruff] + +[tool.ruff.lint] +ignore = ["E741"] diff --git a/scoringrules/_brier.py b/scoringrules/_brier.py index d199a14..8ee6077 100644 --- a/scoringrules/_brier.py +++ b/scoringrules/_brier.py @@ -18,7 +18,7 @@ def brier_score( The BS is formulated as - $$BS(f, y) = (f - y)^2,$$ + $$ BS(f, y) = (f - y)^2, $$ where $f \in [0, 1]$ is the predicted probability of an event and $y \in \{0, 1\}$ the actual outcome. diff --git a/scoringrules/_crps.py b/scoringrules/_crps.py index ca1b13d..ae1a9df 100644 --- a/scoringrules/_crps.py +++ b/scoringrules/_crps.py @@ -560,7 +560,7 @@ def crps_gamma( r"""Compute the closed form of the CRPS for the gamma distribution. It is based on the following formulation from - [Scheuerer and Möller (2015)](doi: doi:10.1214/15-AOAS843): + [Scheuerer and Möller (2015)](https://doi.org/10.1214/15-AOAS843): $$ \mathrm{CRPS}(F_{\alpha, \beta}, y) = y(2F_{\alpha, \beta}(y) - 1) - \frac{\alpha}{\beta} (2 F_{\alpha + 1, \beta}(y) - 1) @@ -622,7 +622,7 @@ def crps_gev( r"""Compute the closed form of the CRPS for the generalised extreme value (GEV) distribution. It is based on the following formulation from - [Friederichs and Thorarinsdottir (2012)](doi/10.1002/env.2176): + [Friederichs and Thorarinsdottir (2012)](https://doi.org/10.1002/env.2176): $$ \text{CRPS}(F_{\xi, \mu, \sigma}, y) = @@ -715,7 +715,7 @@ def crps_gpd( It is based on the following formulation from [Jordan et al. (2019)](https://www.jstatsoft.org/article/view/v090i12): - + $$ \mathrm{CRPS}(F_{M, \xi}, y) = |y| - \frac{2 (1 - M)}{1 - \xi} \left( 1 - (1 - F_{\xi}(y))^{1 - \xi} \right) @@ -774,19 +774,19 @@ def crps_gtclogistic( ) -> "ArrayLike": r"""Compute the closed form of the CRPS for the generalised truncated and censored logistic distribution. - $$ \mathrm{CRPS}(F_{l, L}^{u, U}, y) = |y - z| + uU^{2} - lL^{2} - \left( \frac{1 - L - U}{F(u) - F(l)} \right) z \left( \frac{(1 - 2L) F(u) + (1 - 2U) F(l)}{1 - L - U} \right) - \left( \frac{1 - L - U}{F(u) - F(l)} \right) \left( 2 \logF(-z) - 2G(u)U - 2 G(l)L \right) - \left( \frac{1 - L - U}{F(u) - F(l)} \right)^{2} \left( H(u) - H(l) \right), $$ - + $$ \mathrm{CRPS}(F_{l, L}^{u, U}, y) = |y - z| + uU^{2} - lL^{2} - \left( \frac{1 - L - U}{F(u) - F(l)} \right) z \left( \frac{(1 - 2L) F(u) + (1 - 2U) F(l)}{1 - L - U} \right) - \left( \frac{1 - L - U}{F(u) - F(l)} \right) \left( 2 \log F(-z) - 2G(u)U - 2 G(l)L \right) - \left( \frac{1 - L - U}{F(u) - F(l)} \right)^{2} \left( H(u) - H(l) \right), $$ + $$ \mathrm{CRPS}(F_{l, L, \mu, \sigma}^{u, U}, y) = \sigma \mathrm{CRPS}(F_{(l - \mu)/\sigma, L}^{(u - \mu)/\sigma, U}, \frac{y - \mu}{\sigma}), $$ - + $$G(x) = xF(x) + \log F(-x),$$ - + $$H(x) = F(x) - xF(x)^{2} + (1 - 2F(x))\log F(-x),$$ - where $F$ is the CDF of the standard logistic distribution, $F_{l, L, \mu, \sigma}^{u, U}$ - is the CDF of the logistic distribution truncated below at $l$ and above at $u$, - with point masses $L, U > 0$ at the lower and upper boundaries, respectively, and + where $F$ is the CDF of the standard logistic distribution, $F_{l, L, \mu, \sigma}^{u, U}$ + is the CDF of the logistic distribution truncated below at $l$ and above at $u$, + with point masses $L, U > 0$ at the lower and upper boundaries, respectively, and location and scale parameters $\mu$ and $\sigma > 0$. - + Parameters ---------- observation: ArrayLike @@ -808,13 +808,15 @@ def crps_gtclogistic( ------- crps: array_like The CRPS between gtcLogistic(location, scale, lower, upper, lmass, umass) and obs. - + Examples -------- >>> import scoringrules as sr >>> sr.crps_gtclogistic(0.0, 0.1, 0.4, -1.0, 1.0, 0.1, 0.1) """ - return crps.gtclogistic(observation, location, scale, lower, upper, lmass, umass, backend=backend) + return crps.gtclogistic( + observation, location, scale, lower, upper, lmass, umass, backend=backend + ) def crps_tlogistic( @@ -829,9 +831,9 @@ def crps_tlogistic( ) -> "ArrayLike": r"""Compute the closed form of the CRPS for the truncated logistic distribution. - It is based on the formulation for the generalised truncated and censored logistic + It is based on the formulation for the generalised truncated and censored logistic distribution with lmass and umass set to zero. - + Parameters ---------- observation: ArrayLike @@ -855,7 +857,9 @@ def crps_tlogistic( >>> import scoringrules as sr >>> sr.crps_tlogistic(0.0, 0.1, 0.4, -1.0, 1.0) """ - return crps.gtclogistic(observation, location, scale, lower, upper, 0.0, 0.0, backend=backend) + return crps.gtclogistic( + observation, location, scale, lower, upper, 0.0, 0.0, backend=backend + ) def crps_clogistic( @@ -870,7 +874,7 @@ def crps_clogistic( ) -> "ArrayLike": r"""Compute the closed form of the CRPS for the censored logistic distribution. - It is based on the formulation for the generalised truncated and censored logistic distribution with + It is based on the formulation for the generalised truncated and censored logistic distribution with lmass and umass set to the tail probabilities of the predictive distribution. Parameters @@ -898,7 +902,9 @@ def crps_clogistic( """ lmass = stats._logis_cdf((lower - location) / scale) umass = 1 - stats._logis_cdf((upper - location) / scale) - return crps.gtclogistic(observation, location, scale, lower, upper, lmass, umass, backend=backend) + return crps.gtclogistic( + observation, location, scale, lower, upper, lmass, umass, backend=backend + ) def crps_gtcnormal( @@ -913,18 +919,17 @@ def crps_gtcnormal( *, backend: "Backend" = None, ) -> "ArrayLike": - r"""Compute the closed form of the CRPS for the generalised truncated and censored normal distribution. It is based on the following formulation from [Jordan et al. (2019)](https://www.jstatsoft.org/article/view/v090i12): $$ \mathrm{CRPS}(F_{l, L}^{u, U}, y) = |y - z| + uU^{2} - lL^{2} + \left( \frac{1 - L - U}{\Phi(u) - \Phi(l)} \right) z \left( 2 \Phi(z) - \frac{(1 - 2L) \Phi(u) + (1 - 2U) \Phi(l)}{1 - L - U} \right) + \left( \frac{1 - L - U}{\Phi(u) - \Phi(l)} \right) \left( 2 \phi(z) - 2 \phi(u)U - 2 \phi(l)L \right) - \left( \frac{1 - L - U}{\Phi(u) - \Phi(l)} \right)^{2} \left( \frac{1}{\sqrt{\pi}} \right) \left( \Phi(u \sqrt{2}) - \Phi(l \sqrt{2}) \right), $$ - + $$ \mathrm{CRPS}(F_{l, L, \mu, \sigma}^{u, U}, y) = \sigma \mathrm{CRPS}(F_{(l - \mu)/\sigma, L}^{(u - \mu)/\sigma, U}, \frac{y - \mu}{\sigma}), $$ where $\Phi$ and $\phi$ are respectively the CDF and PDF of the standard normal - distribution, $F_{l, L, \mu, \sigma}^{u, U}$ is the CDF of the normal distribution + distribution, $F_{l, L, \mu, \sigma}^{u, U}$ is the CDF of the normal distribution truncated below at $l$ and above at $u$, with point masses $L, U > 0$ at the lower and upper boundaries, respectively, and location and scale parameters $\mu$ and $\sigma > 0$. $F_{l, L}^{u, U} = F_{l, L, 0, 1}^{u, U}$. @@ -934,7 +939,9 @@ def crps_gtcnormal( >>> import scoring rules as sr >>> sr.crps_gtcnormal(0.0, 0.1, 0.4, -1.0, 1.0, 0.1, 0.1) """ - return crps.gtcnormal(observation, location, scale, lower, upper, lmass, umass, backend=backend) + return crps.gtcnormal( + observation, location, scale, lower, upper, lmass, umass, backend=backend + ) def crps_tnormal( @@ -949,9 +956,9 @@ def crps_tnormal( ) -> "ArrayLike": r"""Compute the closed form of the CRPS for the truncated normal distribution. - It is based on the formulation for the generalised truncated and censored normal distribution with + It is based on the formulation for the generalised truncated and censored normal distribution with distribution with lmass and umass set to zero. - + Parameters ---------- observation: ArrayLike @@ -975,7 +982,9 @@ def crps_tnormal( >>> import scoringrules as sr >>> sr.crps_tnormal(0.0, 0.1, 0.4, -1.0, 1.0) """ - return crps.gtcnormal(observation, location, scale, lower, upper, 0.0, 0.0, backend=backend) + return crps.gtcnormal( + observation, location, scale, lower, upper, 0.0, 0.0, backend=backend + ) def crps_cnormal( @@ -990,7 +999,7 @@ def crps_cnormal( ) -> "ArrayLike": r"""Compute the closed form of the CRPS for the censored normal distribution. - It is based on the formulation for the generalised truncated and censored normal distribution with + It is based on the formulation for the generalised truncated and censored normal distribution with lmass and umass set to the tail probabilities of the predictive distribution. Parameters @@ -1018,12 +1027,14 @@ def crps_cnormal( """ lmass = stats._norm_cdf((lower - location) / scale) umass = 1 - stats._norm_cdf((upper - location) / scale) - return crps.gtcnormal(observation, location, scale, lower, upper, lmass, umass, backend=backend) + return crps.gtcnormal( + observation, location, scale, lower, upper, lmass, umass, backend=backend + ) def crps_gtct( observation: "ArrayLike", - df : "ArrayLike", + df: "ArrayLike", /, location: "ArrayLike" = 0.0, scale: "ArrayLike" = 1.0, @@ -1040,17 +1051,17 @@ def crps_gtct( [Jordan et al. (2019)](https://www.jstatsoft.org/article/view/v090i12): $$ \mathrm{CRPS}(F_{l, L, \nu}^{u, U}, y) = |y - z| + uU^{2} - lL^{2} + \left( \frac{1 - L - U}{F_{\nu}(u) - F_{\nu}(l)} \right) z \left( 2 F_{\nu}(z) - \frac{(1 - 2L) F_{\nu}(u) + (1 - 2U) F_{\nu}(l)}{1 - L - U} \right) - \left( \frac{1 - L - U}{F_{\nu}(u) - F_{\nu}(l)} \right) \left( 2 G_{\nu}(z) - 2 G_{\nu}(u)U - 2 G_{\nu}(l)L \right) - \left( \frac{1 - L - U}{F_{\nu}(u) - F_{\nu}(l)} \right)^{2} \bar{B}_{\nu} \left( H_{\nu}(u) - H_{\nu}(l) \right), $$ - + $$ \mathrm{CRPS}(F_{l, L, \nu, \mu, \sigma}^{u, U}, y) = \sigma \mathrm{CRPS}(F_{(l - \mu)/\sigma, L, \nu}^{(u - \mu)/\sigma, U}, \frac{y - \mu}{\sigma}), $$ $$ G_{\nu}(x) = - \left( \frac{\nu + x^{2}}{\nu - 1} \right) f_{\nu}(x), $$ - + $$ H_{\nu}(x) = \frac{1}{2} + \frac{1}{2} \mathrm{sgn}(x) I \left( \frac{1}{2}, \nu - \frac{1}{2}, \frac{x^{2}}{\nu + x^{2}} \right), $$ - + $$ \bar{B}_{\nu} = \left( \frac{2 \sqrt{\nu}}{\nu - 1} \right) \frac{B(\frac{1}{2}, \nu - \frac{1}{2})}{B(\frac{1}{2}, \frac{\nu}{2})^{2}}, $$ where $F_{\nu}$ is the CDF of the standard t distribution with $\nu > 1$ degrees of freedom, - distribution, $F_{l, L, \nu, \mu, \sigma}^{u, U}$ is the CDF of the t distribution + distribution, $F_{l, L, \nu, \mu, \sigma}^{u, U}$ is the CDF of the t distribution truncated below at $l$ and above at $u$, with point masses $L, U > 0$ at the lower and upper boundaries, respectively, and degrees of freedom, location and scale parameters $\nu > 1$, $\mu$ and $\sigma > 0$. $F_{l, L, \nu}^{u, U} = F_{l, L, \nu, 0, 1}^{u, U}$. @@ -1084,12 +1095,14 @@ def crps_gtct( >>> import scoringrules as sr >>> sr.crps_gtct(0.0, 2.0, 0.1, 0.4, -1.0, 1.0, 0.1, 0.1) """ - return crps.gtct(observation, df, location, scale, lower, upper, lmass, umass, backend=backend) + return crps.gtct( + observation, df, location, scale, lower, upper, lmass, umass, backend=backend + ) def crps_tt( observation: "ArrayLike", - df : "ArrayLike", + df: "ArrayLike", /, location: "ArrayLike" = 0.0, scale: "ArrayLike" = 1.0, @@ -1100,9 +1113,9 @@ def crps_tt( ) -> "ArrayLike": r"""Compute the closed form of the CRPS for the truncated t distribution. - It is based on the formulation for the generalised truncated and censored t distribution with + It is based on the formulation for the generalised truncated and censored t distribution with lmass and umass set to zero. - + Parameters ---------- observation: ArrayLike @@ -1128,12 +1141,14 @@ def crps_tt( >>> import scoringrules as sr >>> sr.crps_tt(0.0, 2.0, 0.1, 0.4, -1.0, 1.0) """ - return crps.gtct(observation, df, location, scale, lower, upper, 0.0, 0.0, backend=backend) + return crps.gtct( + observation, df, location, scale, lower, upper, 0.0, 0.0, backend=backend + ) def crps_ct( observation: "ArrayLike", - df : "ArrayLike", + df: "ArrayLike", /, location: "ArrayLike" = 0.0, scale: "ArrayLike" = 1.0, @@ -1144,7 +1159,7 @@ def crps_ct( ) -> "ArrayLike": r"""Compute the closed form of the CRPS for the censored t distribution. - It is based on the formulation for the generalised truncated and censored t distribution with + It is based on the formulation for the generalised truncated and censored t distribution with lmass and umass set to the tail probabilities of the predictive distribution. Parameters @@ -1174,7 +1189,9 @@ def crps_ct( """ lmass = stats._t_cdf((lower - location) / scale, df) umass = 1 - stats._t_cdf((upper - location) / scale, df) - return crps.gtct(observation, df, location, scale, lower, upper, lmass, umass, backend=backend) + return crps.gtct( + observation, df, location, scale, lower, upper, lmass, umass, backend=backend + ) def crps_hypergeometric( @@ -1511,7 +1528,7 @@ def crps_poisson( backend: "Backend" = None, ) -> "ArrayLike": r"""Compute the closed form of the CRPS for the Poisson distribution. - + It is based on the following formulation from [Wei and Held (2014)](https://link.springer.com/article/10.1007/s11749-014-0380-8): @@ -1526,12 +1543,12 @@ def crps_poisson( The observed values. mean: ArrayLike Mean parameter of the forecast exponential distribution. - + Returns ------- crps: array_like The CRPS between Pois(mean) and obs. - + Examples -------- >>> import scoringrules as sr @@ -1554,13 +1571,18 @@ def crps_t( It is based on the following formulation from [Jordan et al. (2019)](https://www.jstatsoft.org/article/view/v090i12): - $$ \mathrm{CRPS}(F, y) = \sigma \left\{ \omega (2 F_{\nu} (\omega) - 1) + 2 f_{\nu} \left( \frac{\nu + \omega^{2}}{\nu - 1} \right) - \frac{2 \sqrt{\nu}}{\nu - 1} \frac{B(\frac{1}{2}, \nu - \frac{1}{2})}{B(\frac{1}{2}, \frac{\nu}{2}^{2})} \right},$$ + $$ + \mathrm{CRPS}(F, y) = \sigma \left\{ \omega (2 F_{\nu} (\omega) - 1) + + 2 f_{\nu} \left( \frac{\nu + \omega^{2}}{\nu - 1} \right) + - \frac{2 \sqrt{\nu}}{\nu - 1} \frac{B(\frac{1}{2}, + \nu - \frac{1}{2})}{B(\frac{1}{2}, \frac{\nu}{2}^{2})} \right\}, + $$ - where $\omega = (y - \mu)/\sigma$, where $\nu > 1, \mu$, and $\sigma > 0$ are the - degrees of freedom, location, and scale parameters respectively of the Student's t + where $\omega = (y - \mu)/\sigma$, where $\nu > 1, \mu$, and $\sigma > 0$ are the + degrees of freedom, location, and scale parameters respectively of the Student's t distribution, and $f_{\nu}$ and $F_{\nu}$ are the PDF and CDF of the standard Student's t distribution with $\nu$ degrees of freedom. - + Parameters ---------- observation: ArrayLike @@ -1596,15 +1618,15 @@ def crps_uniform( backend: "Backend" = None, ) -> "ArrayLike": r"""Compute the closed form of the CRPS for the uniform distribution. - + It is based on the following formulation from [Jordan et al. (2019)](https://www.jstatsoft.org/article/view/v090i12): $$ \mathrm{CRPS}(\mathcal{U}_{L}^{U}(l, u), y) = (u - l) \left\{ | \frac{y - l}{u - l} - F \left( \frac{y - l}{u - l} \right) | + F \left( \frac{y - l}{u - l} \right)^{2} (1 - L - U) - F \left( \frac{y - l}{u - l} \right) (1 - 2L) + \frac{(1 - L - U)^{2}}{3} + (1 - L)U \right\},$$ - where $\mathcal{U}_{L}^{U}(l, u)$ is the uniform distribution with lower bound $l$, + where $\mathcal{U}_{L}^{U}(l, u)$ is the uniform distribution with lower bound $l$, upper bound $u > l$, point mass $L$ on the lower bound, and point mass $U$ on the upper bound. - We must have that $L, U \ge 0, L + U < 1$. + We must have that $L, U \ge 0, L + U < 1$. Parameters ---------- diff --git a/scoringrules/_logs.py b/scoringrules/_logs.py index 806d2bf..2298f57 100644 --- a/scoringrules/_logs.py +++ b/scoringrules/_logs.py @@ -21,7 +21,7 @@ def logs_normal( Parameters ---------- - observations: ArrayLike + observation: ArrayLike The observed values. mu: ArrayLike Mean of the forecast normal distribution. diff --git a/scoringrules/backend/jax.py b/scoringrules/backend/jax.py index 2bd3486..8d11332 100644 --- a/scoringrules/backend/jax.py +++ b/scoringrules/backend/jax.py @@ -208,8 +208,8 @@ def factorial(self, n: "ArrayLike") -> "ArrayLike": return jsp.special.factorial(n) def hypergeometric(self, a: "Array", b: "Array", c: "Array", z: "Array"): - #return jsp.special.hyp2f1(a, b, c, z) - raise NotImplementedError + # return jsp.special.hyp2f1(a, b, c, z) + raise NotImplementedError def comb(self, n: "ArrayLike", k: "ArrayLike") -> "ArrayLike": return jsp.special.factorial(n) // ( diff --git a/scoringrules/core/crps/_closed.py b/scoringrules/core/crps/_closed.py index 557d4cc..907437a 100644 --- a/scoringrules/core/crps/_closed.py +++ b/scoringrules/core/crps/_closed.py @@ -254,11 +254,20 @@ def gpd( def gtclogistic( - obs: "ArrayLike", location: "ArrayLike", scale: "ArrayLike", lower: "ArrayLike", upper: "ArrayLike", lmass: "ArrayLike", umass: "ArrayLike", backend: "Backend" = None + obs: "ArrayLike", + location: "ArrayLike", + scale: "ArrayLike", + lower: "ArrayLike", + upper: "ArrayLike", + lmass: "ArrayLike", + umass: "ArrayLike", + backend: "Backend" = None, ) -> "Array": """Compute the CRPS for the generalised truncated and censored logistic distribution.""" B = backends.active if backend is None else backends[backend] - obs, mu, sigma, lower, upper, lmass, umass = map(B.asarray, (obs, location, scale, lower, upper, lmass, umass)) + obs, mu, sigma, lower, upper, lmass, umass = map( + B.asarray, (obs, location, scale, lower, upper, lmass, umass) + ) ω = (obs - mu) / sigma u = (upper - mu) / sigma l = (lower - mu) / sigma @@ -280,7 +289,7 @@ def gtclogistic( G_u = B.where(u_inf, 0.0, u * F_u + B.log(F_mu)) G_l = B.where(l_inf, 0.0, l * F_l + B.log(F_ml)) H_u = B.where(u_inf, 1.0, F_u - u * F_u**2 + (1 - 2 * F_u) * B.log(F_mu)) - H_l = B.where(l_inf, 0.0, F_l - l * F_l**2 + (1 - 2 * F_l) * B.log(F_ml)) + H_l = B.where(l_inf, 0.0, F_l - l * F_l**2 + (1 - 2 * F_l) * B.log(F_ml)) c = (1 - lmass - umass) / (F_u - F_l) @@ -295,11 +304,20 @@ def gtclogistic( def gtcnormal( - obs: "ArrayLike", location: "ArrayLike", scale: "ArrayLike", lower: "ArrayLike", upper: "ArrayLike", lmass: "ArrayLike", umass: "ArrayLike", backend: "Backend" = None + obs: "ArrayLike", + location: "ArrayLike", + scale: "ArrayLike", + lower: "ArrayLike", + upper: "ArrayLike", + lmass: "ArrayLike", + umass: "ArrayLike", + backend: "Backend" = None, ) -> "Array": """Compute the CRPS for the generalised truncated and censored normal distribution.""" B = backends.active if backend is None else backends[backend] - mu, sigma, lower, upper, lmass, umass, obs = map(B.asarray, (location, scale, lower, upper, lmass, umass, obs)) + mu, sigma, lower, upper, lmass, umass, obs = map( + B.asarray, (location, scale, lower, upper, lmass, umass, obs) + ) ω = (obs - mu) / sigma u = (upper - mu) / sigma l = (lower - mu) / sigma @@ -324,18 +342,35 @@ def gtcnormal( c = (1 - lmass - umass) / (F_u - F_l) s1 = B.abs(ω - z) + s1_u - s1_l - s2 = c * z * (2 * F_z - ((1 - 2 * lmass) * F_u + (1 - 2 * umass) * F_l) / (1 - lmass - umass)) + s2 = ( + c + * z + * ( + 2 * F_z + - ((1 - 2 * lmass) * F_u + (1 - 2 * umass) * F_l) / (1 - lmass - umass) + ) + ) s3 = c * (2 * f_z - 2 * f_u * umass - 2 * f_l * lmass) s4 = c**2 * (F_u2 - F_l2) / B.sqrt(B.pi) return sigma * (s1 + s2 + s3 - s4) def gtct( - obs: "ArrayLike", df: "ArrayLike", location: "ArrayLike", scale: "ArrayLike", lower: "ArrayLike", upper: "ArrayLike", lmass: "ArrayLike", umass: "ArrayLike", backend: "Backend" = None + obs: "ArrayLike", + df: "ArrayLike", + location: "ArrayLike", + scale: "ArrayLike", + lower: "ArrayLike", + upper: "ArrayLike", + lmass: "ArrayLike", + umass: "ArrayLike", + backend: "Backend" = None, ) -> "Array": """Compute the CRPS for the generalised truncated and censored t distribution.""" B = backends.active if backend is None else backends[backend] - df, mu, sigma, lower, upper, lmass, umass, obs = map(B.asarray, (df, location, scale, lower, upper, lmass, umass, obs)) + df, mu, sigma, lower, upper, lmass, umass, obs = map( + B.asarray, (df, location, scale, lower, upper, lmass, umass, obs) + ) ω = (obs - mu) / sigma u = (upper - mu) / sigma l = (lower - mu) / sigma @@ -355,9 +390,9 @@ def gtct( s1_u = B.where(u_inf and umass == 0.0, 0.0, u * umass**2) s1_l = B.where(l_inf and lmass == 0.0, 0.0, l * lmass**2) - G_u = B.where(u_inf, 0.0, - f_u * (df + u**2) / (df - 1)) - G_l = B.where(l_inf, 0.0, - f_l * (df + l**2) / (df - 1)) - G_z = - f_z * (df + z**2) / (df - 1) + G_u = B.where(u_inf, 0.0, -f_u * (df + u**2) / (df - 1)) + G_l = B.where(l_inf, 0.0, -f_l * (df + l**2) / (df - 1)) + G_z = -f_z * (df + z**2) / (df - 1) I_u = B.where(u_inf, 1.0, B.betainc(1 / 2, df - 1 / 2, (u**2) / (df + u**2))) I_l = B.where(l_inf, 1.0, B.betainc(1 / 2, df - 1 / 2, (l**2) / (df + l**2))) @@ -366,12 +401,23 @@ def gtct( H_u = (sgn_u * I_u + 1) / 2 H_l = (sgn_l * I_l + 1) / 2 - Bbar = (2 * B.sqrt(df) / (df - 1)) * B.beta(1 / 2, df - 1 / 2) / (B.beta(1 / 2, df / 2)**2) + Bbar = ( + (2 * B.sqrt(df) / (df - 1)) + * B.beta(1 / 2, df - 1 / 2) + / (B.beta(1 / 2, df / 2) ** 2) + ) c = (1 - lmass - umass) / (F_u - F_l) s1 = B.abs(ω - z) + s1_u - s1_l - s2 = c * z * (2 * F_z - ((1 - 2 * lmass) * F_u + (1 - 2 * umass) * F_l) / (1 - lmass - umass)) + s2 = ( + c + * z + * ( + 2 * F_z + - ((1 - 2 * lmass) * F_u + (1 - 2 * umass) * F_l) / (1 - lmass - umass) + ) + ) s3 = 2 * c * (G_z - G_u * umass - G_l * lmass) s4 = c**2 * Bbar * (H_u - H_l) return sigma * (s1 + s2 - s3 - s4) @@ -562,12 +608,20 @@ def poisson( f_m = _pois_pdf(B.floor(obs), mean, backend=backend) I0 = B.mbessel0(2 * mean) I1 = B.mbessel1(2 * mean) - s = (obs - mean) * (2 * F_m - 1) + 2 * mean * f_m - mean * B.exp(-2 * mean) * (I0 + I1) + s = ( + (obs - mean) * (2 * F_m - 1) + + 2 * mean * f_m + - mean * B.exp(-2 * mean) * (I0 + I1) + ) return s def t( - obs: "ArrayLike", df: "ArrayLike", location: "ArrayLike", scale: "ArrayLike", backend: "Backend" = None + obs: "ArrayLike", + df: "ArrayLike", + location: "ArrayLike", + scale: "ArrayLike", + backend: "Backend" = None, ) -> "Array": """Compute the CRPS for the t distribution.""" B = backends.active if backend is None else backends[backend] @@ -575,22 +629,37 @@ def t( z = (obs - mu) / sigma F_z = _t_cdf(z, df, backend=backend) f_z = _t_pdf(z, df, backend=backend) - G_z = (df + z**2) / (df - 1) + G_z = (df + z**2) / (df - 1) s1 = z * (2 * F_z - 1) s2 = 2 * f_z * G_z - s3 = (2 * B.sqrt(df) / (df - 1)) * B.beta(1 / 2, df - 1 / 2) / (B.beta(1 / 2, df / 2)**2) + s3 = ( + (2 * B.sqrt(df) / (df - 1)) + * B.beta(1 / 2, df - 1 / 2) + / (B.beta(1 / 2, df / 2) ** 2) + ) return sigma * (s1 + s2 - s3) def uniform( - obs: "ArrayLike", min: "ArrayLike", max: "ArrayLike", lmass: "ArrayLike", umass: "ArrayLike", backend: "Backend" = None + obs: "ArrayLike", + min: "ArrayLike", + max: "ArrayLike", + lmass: "ArrayLike", + umass: "ArrayLike", + backend: "Backend" = None, ) -> "Array": """Compute the CRPS for the uniform distribution.""" B = backends.active if backend is None else backends[backend] min, max, lmass, umass, obs = map(B.asarray, (min, max, lmass, umass, obs)) ω = (obs - min) / (max - min) - F_ω = B.minimum(B.maximum(ω, B.asarray(0.0)), B.asarray(1.0)) - s = B.abs(ω - F_ω) + (F_ω**2) * (1 - lmass - umass) - F_ω * (1 - 2 * lmass) + ((1 - lmass - umass)**2) / 3 + (1 - lmass) * umass + F_ω = B.minimum(B.maximum(ω, B.asarray(0)), B.asarray(1)) + s = ( + B.abs(ω - F_ω) + + (F_ω**2) * (1 - lmass - umass) + - F_ω * (1 - 2 * lmass) + + ((1 - lmass - umass) ** 2) / 3 + + (1 - lmass) * umass + ) return (max - min) * s diff --git a/scoringrules/core/stats.py b/scoringrules/core/stats.py index 0fd2c05..787a358 100644 --- a/scoringrules/core/stats.py +++ b/scoringrules/core/stats.py @@ -51,7 +51,11 @@ def _pois_pdf(x: "ArrayLike", mean: "ArrayLike", backend: "Backend" = None) -> " """Probability mass function for the Poisson distribution.""" B = backends.active if backend is None else backends[backend] x_plus = B.abs(x) - d = B.where(B.floor(x_plus) < x_plus, 0.0, mean**(x_plus) * B.exp(-mean) / B.factorial(x_plus)) + d = B.where( + B.floor(x_plus) < x_plus, + 0.0, + mean ** (x_plus) * B.exp(-mean) / B.factorial(x_plus), + ) return B.where(mean < 0.0, B.nan, B.where(x < 0.0, 0.0, d)) diff --git a/tests/test_crps.py b/tests/test_crps.py index f1e39e8..25fa6a7 100644 --- a/tests/test_crps.py +++ b/tests/test_crps.py @@ -234,11 +234,21 @@ def test_gpd(backend): @pytest.mark.parametrize("backend", BACKENDS) def test_gtclogis(backend): - obs, location, scale, lower, upper, lmass, umass = 1.8, -3.0, 3.3, -5.0, 4.7, 0.1, 0.15 + obs, location, scale, lower, upper, lmass, umass = ( + 1.8, + -3.0, + 3.3, + -5.0, + 4.7, + 0.1, + 0.15, + ) expected = 1.599721 - res = _crps.crps_gtclogistic(obs, location, scale, lower, upper, lmass, umass, backend=backend) + res = _crps.crps_gtclogistic( + obs, location, scale, lower, upper, lmass, umass, backend=backend + ) assert np.isclose(res, expected) - + # aligns with crps_logistic res0 = _crps.crps_logistic(obs, location, scale, backend=backend) res = _crps.crps_gtclogistic(obs, location, scale, backend=backend) @@ -278,9 +288,19 @@ def test_clogis(backend): @pytest.mark.parametrize("backend", BACKENDS) def test_gtcnormal(backend): - obs, location, scale, lower, upper, lmass, umass = 0.9, -2.3, 4.1, -7.3, 1.7, 0.0, 0.21 + obs, location, scale, lower, upper, lmass, umass = ( + 0.9, + -2.3, + 4.1, + -7.3, + 1.7, + 0.0, + 0.21, + ) expected = 1.422805 - res = _crps.crps_gtcnormal(obs, location, scale, lower, upper, lmass, umass, backend=backend) + res = _crps.crps_gtcnormal( + obs, location, scale, lower, upper, lmass, umass, backend=backend + ) assert np.isclose(res, expected) # aligns with crps_normal @@ -324,9 +344,20 @@ def test_cnormal(backend): def test_gtct(backend): if backend in ["jax", "torch", "tensorflow"]: pytest.skip("Not implemented in torch or tensorflow backends") - obs, df, location, scale, lower, upper, lmass, umass = 0.9, 20.1, -2.3, 4.1, -7.3, 1.7, 0.0, 0.21 + obs, df, location, scale, lower, upper, lmass, umass = ( + 0.9, + 20.1, + -2.3, + 4.1, + -7.3, + 1.7, + 0.0, + 0.21, + ) expected = 1.423042 - res = _crps.crps_gtct(obs, df, location, scale, lower, upper, lmass, umass, backend=backend) + res = _crps.crps_gtct( + obs, df, location, scale, lower, upper, lmass, umass, backend=backend + ) assert np.isclose(res, expected) # aligns with crps_t @@ -344,14 +375,14 @@ def test_gtct(backend): def test_tt(backend): if backend in ["jax", "torch", "tensorflow"]: pytest.skip("Not implemented in torch or tensorflow backends") - + obs, df, location, scale, lower, upper = -1.0, 2.9, 3.1, 4.2, 1.5, 17.3 expected = 5.084272 res = _crps.crps_tt(obs, df, location, scale, lower, upper, backend=backend) assert np.isclose(res, expected) # aligns with crps_t - res0 = _crps.crps_t(obs, df ,location, scale, backend=backend) + res0 = _crps.crps_t(obs, 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