properscoring

library0.1pypypi✓ verified 88d ago

A Python library for evaluating probabilistic forecasts using proper scoring rules. Version 0.1 is the only release; the project is in maintenance mode with no recent updates. It provides implementations of the Continuous Ranked Probability Score (CRPS), Brier score, and related metrics for forecast verification.

ai-ml data

pip install properscoring

github ↗package ↗

INSTALL

IMPORT

SIG · PROPERSCORING

properscoring

ai-mlpythonv0.1

Install

7.2s avg

Import

2431ms

Disk

230MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.1 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 2.458s · 230.8MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 7.2s · import 2.404s · 222MB

230MB installed

● package 230MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

crps_ensemble

✓ from properscoring import crps_ensemble

Most commonly used function for CRPS from ensemble forecasts.

crps_gaussian

✓ from properscoring import crps_gaussian

For parametric Gaussian forecasts.

Compute the Continuous Ranked Probability Score (CRPS) for ensemble forecasts against observations.

import numpy as np from properscoring import crps_ensemble # Ensemble forecasts (10 members) ensemble = np.random.randn(10, 100) # Observations observations = np.random.randn(100) # Calculate CRPS for each observation crps = crps_ensemble(observations, ensemble) print(crps.mean())

Debug

Known issues

gotchaInput arrays for `crps_ensemble` must be 1D or 2D with observations as first argument and ensemble as second. If dimensions are mismatched (e.g., shapes (n,) vs (m, n) transposed), you may get incorrect results or broadcasting issues.

fix

Ensure observations have shape (n,) or (1, n) and ensemble has shape (n_ens, n); use `ensemble.T` if needed.

affects: <=0.1

gotchaThe library has no official support for Python 3.10+ (no wheels for recent Python versions). Installing on Python 3.10 or 3.11 requires building from source, which may fail if compilation tools are missing.

fix

Use Python 3.8 or 3.9 for guaranteed compatibility, or install via conda-forge: `conda install -c conda-forge properscoring`.

affects: >=0.1

breakingIn v0.1, `crps_ensemble` does not handle NaN values; passing arrays with NaN can cause silent errors or incorrect results.

fix

Manually remove or impute NaN values before calling the function.

affects: 0.1

Errors

Common errors & fixes

ImportError: cannot import name 'crps_ensemble'

Older version or mis-installation. The import name is correct; ensure properscoring is installed.

fix

Run `pip install properscoring` and verify import with `import properscoring`.

ValueError: operands could not be broadcast together with shapes ...

Input arrays of incompatible shapes to `crps_ensemble`.

fix

Reshape: observations should be (n,) or (1, n), ensemble should be (m, n). Use `np.atleast_2d(obs)` and check ensemble shape.

TypeError: 'numpy.float64' object is not callable

Accidentally shadowed the `crps_ensemble` function with a variable (e.g., `crps_ensemble = ...`).

fix

Restart the interpreter and avoid using the function name as a variable.

Upgrade

Version history

0.1latest on PyPI · released Nov 12, 2015

Audit

Dependencies

numpyrequiredCore dependency for array operations and numerical computations.

scipyrequiredRequired for special functions (e.g., erf) used in CRPS calculations.

Agent activity

7 hits · last 30 days

node

Resources

githubhttps://github.com/TheClimateCorporation/properscoring ↗

packagehttps://pypi.org/project/properscoring/ ↗