pysteps.verification.detcatscores.det_cat_fct_compute

pysteps.verification.detcatscores.det_cat_fct_compute#

pysteps.verification.detcatscores.det_cat_fct_compute(contab, scores='')#

Compute simple and skill scores for deterministic categorical (dichotomous) forecasts from a contingency table object.

Parameters:

  • contab (dict) – A contingency table object initialized with pysteps.verification.detcatscores.det_cat_fct_init and populated with pysteps.verification.detcatscores.det_cat_fct_accum.

  • scores ({string, list of strings}, optional) –

    The name(s) of the scores. The default, scores=””, will compute all available scores. The available score names a

    Name

    Description

    ACC

    accuracy (proportion correct)

    BIAS

    frequency bias

    CSI

    critical success index (threat score)

    ETS

    equitable threat score

    F1

    the harmonic mean of precision and sensitivity

    FA

    false alarm rate (prob. of false detection, fall-out, false positive rate)

    FAR

    false alarm ratio (false discovery rate)

    GSS

    Gilbert skill score (equitable threat score)

    HK

    Hanssen-Kuipers discriminant (Pierce skill score)

    HSS

    Heidke skill score

    MCC

    Matthews correlation coefficient

    POD

    probability of detection (hit rate, sensitivity, recall, true positive rate)

    SEDI

    symmetric extremal dependency index

Returns:

result – Dictionary containing the verification results.

Return type:

dict

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