Metadata-Version: 1.1
Name: properscoring
Version: 0.1
Summary: Proper scoring rules in Python
Home-page: https://github.com/TheClimateCorporation/properscoring
Author: The Climate Corporation
Author-email: eng@climate.com
License: Apache
Description: properscoring
        =============
        
        .. image:: https://travis-ci.org/TheClimateCorporation/properscoring.svg?branch=master
            :target: https://travis-ci.org/TheClimateCorporation/properscoring
        
        `Proper scoring rules`_ for evaluating probabilistic forecasts in Python.
        Evaluation methods that are "strictly proper" cannot be artificially improved
        through hedging, which makes them fair methods for accessing the accuracy of
        probabilistic forecasts. In particular, these rules are often used for
        evaluating weather forecasts.
        
        .. _Proper scoring rules: https://www.stat.washington.edu/raftery/Research/PDF/Gneiting2007jasa.pdf
        
        properscoring runs on both Python 2 and 3. It requires NumPy (1.8 or
        later) and SciPy (any recent version should be fine). Numba is optional,
        but highly encouraged: it enables significant speedups (e.g., 20x faster)
        for ``crps_ensemble`` and ``threshold_brier_score``.
        
        To install, use pip: ``pip install properscoring``.
        
        Example: five ways to calculate CRPS
        ------------------------------------
        
        This library focuses on the closely related
        `Continuous Ranked Probability Score`_ (CRPS) and `Brier Score`_. We like
        these scores because they are both interpretable (e.g., CRPS is a
        generalization of mean absolute error) and easily calculated from a finite
        number of samples of a probability distribution.
        
        .. _Continuous Ranked Probability Score: http://www.eumetcal.org/resources/ukmeteocal/verification/www/english/msg/ver_prob_forec/uos3b/uos3b_ko1.htm
        .. _Brier score: https://en.wikipedia.org/wiki/Brier_score
        
        We will illustrate how to calculate CRPS against a forecast given by a
        Gaussian random variable. To begin, import properscoring::
        
            import numpy as np
            import properscoring as ps
            from scipy.stats import norm
        
        Exact calculation using ``crps_gaussian`` (this is the fastest method)::
        
            >>>> ps.crps_gaussian(0, mu=0, sig=1)
            0.23369497725510913
        
        Numerical integration with ``crps_quadrature``::
        
            >>> ps.crps_quadrature(0, norm)
            array(0.23369497725510724)
        
        From a finite sample with ``crps_ensemble``::
        
            >>> ensemble = np.random.RandomState(0).randn(1000)
            >>> ps.crps_ensemble(0, ensemble)
            0.2297109370729622
        
        Weighted by PDF values with ``crps_ensemble``::
        
            >>> x = np.linspace(-5, 5, num=1000)
            >>> ps.crps_ensemble(0, x, weights=norm.pdf(x))
            0.23370047937569616
        
        Based on the `threshold decomposition`_ of CRPS with
        ``threshold_brier_score``::
        
            >>> threshold_scores = ps.threshold_brier_score(0, ensemble, threshold=x)
            >>> (x[1] - x[0]) * threshold_scores.sum(axis=-1)
            0.22973090090090081
        
        .. _threshold decomposition: https://www.stat.washington.edu/research/reports/2008/tr533.pdf
        
        In this example, we only scored a single observation/forecast pair. But
        to reliably evaluate a forecast model, you need to average these scores across
        many observations. Fortunately, all scoring rules in properscoring happily
        accept and return observations as multi-dimensional arrays::
        
            >>> ps.crps_gaussian([-2, -1, 0, 1, 2], mu=0, sig=1)
            array([ 1.45279182,  0.60244136,  0.23369498,  0.60244136,  1.45279182])
        
        Once you calculate an average score, is often useful to normalize them
        relative to a baseline forecast to calculate a so-called "skill score",
        defined such that 0 indicates no improvement over the baseline and 1
        indicates a perfect forecast. For example, suppose that our baseline
        forecast is to always predict 0::
        
            >>> obs = [-2, -1, 0, 1, 2]
            >>> baseline_score = ps.crps_ensemble(obs, [0, 0, 0, 0, 0]).mean()
            >>> forecast_score = ps.crps_gaussian(obs, mu=0, sig=1).mean()
            >>> skill = (baseline_score - forecast_score) / baseline_score
            >>> skill
            0.27597311068630859
        
        A standard normal distribution was 28% better at predicting these five
        observations.
        
        API
        ---
        
        properscoring contains optimized and extensively tested routines for
        scoring probability forecasts. These functions currently fall into two
        categories:
        
        * Continuous Ranked Probability Score (CRPS):
        
          - for an ensemble forecast: ``crps_ensemble``
          - for a Gaussian distribution: ``crps_gaussian``
          - for an arbitrary cumulative distribution function: ``crps_quadrature``
        
        * Brier score:
        
          - for binary probability forecasts: ``brier_score``
          - for threshold exceedances with an ensemble forecast: ``threshold_brier_score``
        
        All functions are robust to missing values represented by the floating
        point value ``NaN``.
        
        History
        -------
        
        This library was written by researchers at The Climate Corporation. The
        original authors include Leon Barrett, Stephan Hoyer, Alex Kleeman and
        Drew O'Kane.
        
        License
        -------
        
        Copyright 2015 The Climate Corporation
        
        Licensed under the Apache License, Version 2.0 (the "License");
        you may not use this file except in compliance with the License.
        You may obtain a copy of the License at
        
        http://www.apache.org/licenses/LICENSE-2.0
        
        Unless required by applicable law or agreed to in writing, software
        distributed under the License is distributed on an "AS IS" BASIS,
        WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
        See the License for the specific language governing permissions and
        limitations under the License.
        
        Contributions
        -------------
        
        Outside contributions (bug fixes or new features related to proper scoring
        rules) would be very welcome! Please open a GitHub issue to discuss your
        plans.
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering
