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Metrics undefinedmetricexception
Development build. This page describes
main, not a released package. The latest published Lodestar.Metrics is 0.3.0 — read its documentation.
Thrown when a metric has no value and you asked to be told rather than handed a number.
public sealed class UndefinedMetricException : InvalidOperationExceptionConstructors — the parameterless one carries a default message; the others take a message, and a message with an inner exception.
Example — asking to be told instead of scoring 0.
using Lodestar.Metrics;
int[] yTrue = [0, 0, 1];
int[] yPred = [0, 0, 0];
string what = "nothing was thrown";
try
{
_ = Precision.Score(yTrue, yPred, zeroDivision: ZeroDivision.Throw);
}
catch (UndefinedMetricException error)
{
what = error.Message;
}
string message = what; // => Precision is undefined here: no sample contributes…Remarks — this is the counterpart of scikit-learn's UndefinedMetricWarning, which it does
not
reproduce and deliberately improves on. A warning in Python is easy to miss and easy to filter,
and
the value that comes back with it — 0.0 — is indistinguishable in a report from a genuinely
terrible score. Selecting ZeroDivision.Throw turns that silence into a stack trace naming the
metric.
The trap is reaching for it as the default. It is not, and should not be: parity with scikit-learn
requires the value, so ZeroDivision.Zero is what every precision-family metric starts from.
Throw
is the setting for a pipeline that would rather fail than publish a number nobody can interpret —
which is a reasonable thing to want in CI and a bad thing to want in a dashboard.
Applies to — net10.0, netstandard2.0.
See also — ZeroDivision, Precision.Score, CohenKappa.Score,
the Python equivalence table.
| Member | What it does |
|---|
- 0001-target-framework
- 0002-unicode-comparison-unit
- 0003-provenance-and-licensing
- 0004-levenshtein-myers-backlog
- 0005-hamming-jellyfish-divergence
- 0006-ratcliff-autojunk
- 0007-metaphone-scope
- 0008-italian-enza-nltk-divergence
- 0009-sample-consumes-a-local-feed
- 0010-stop-word-list-provenance
- 0011-persistence-format
- 0012-per-package-versioning
- 0013-sentencepiece-parity-scope
- 0014-precompiled-normalizer
- 0015-sonar-rules-in-the-build
- 0016-metrics-package-placement
- 0017-bpe-parity-scope
- 0018-multiclass-roc-auc-parallelism-is-opt-in
- 0019-the-net-analysers-run-in-the-build-too
- 0020-normalize-is-a-projection-not-a-parameter
- 0021-multioutput-is-a-method-not-an-enum
- 0022-added-token-matching-flags
- 0023-byte-level-decode-substitutes
- 0024-weighted-median-averages-within-scikit-learns-epsilon
- 0025-quickselect-replaces-a-full-sort-for-the-median
- 0026-r2-and-explainedvariance-split-their-undefined-cases-differently
- 0027-r2-and-explainedvariance-vectorize-only-a-single-output
- 0028-log1p-is-kahans-identity-not-math-log-1-plus-x
- 0029-balanced-accuracy-adjusted-is-left-to-ieee-754-at-the-edge
- 0030-cohen-kappa-keeps-scikit-learns-expected-matrix-orientation
- 0031-nosamplecorrect-mirrors-numpys-float64-upcast
- 0032-fbeta-substitutes-tp-predicted-and-support-algebraically
- 0033-compensated-sum-is-neumaiers-variant
- 0034-dropout-is-refused-for-want-of-a-user
- 0035-a-null-pre-split-is-removed-with-invert-not-isolated
- 0036-a-member-may-ship-without-an-oracle-if-it-says-so
- 0037-the-guards-run-before-the-commit
- 0038-the-gate-confronts-an-exception-tag-with-the-page-that-documents-it
- 0039-mutual-information-returns-zero-on-an-empty-input
- 0040-a-curve-is-a-sealed-class-per-curve
- 0041-one-sample-file-per-public-class
- 0042-phonetic-encoders-refuse-a-null-word
- 0043-the-equality-table-is-sized-to-the-pattern
- 0044-compression-belongs-to-the-caller
- 0045-a-console-call-carries-its-reason-on-the-line
- 0046-check-adr-immutable-runs-in-ci-only
- 0047-one-gate-per-kernel-not-one-per-alphabet
- 0048-the-gate-depends-on-the-kernel-and-the-alphabet
- 0049-two-gates-per-kernel-tested-where-the-width-is-known
- 0050-the-sentencepiece-bpe-lineage-stays-a-bpe-model
- benchmark_latest
- decisions
- equivalence
- matplotlib
- migration
- nightly_run
- numpy
- pandas
- performance
- pytorch
- seaborn
- sklearn
- statsmodels