Skip to main content

A collection of metrics for analysing confusion matrices

Project description

# David’s helpful metrics library

There are many different ways to evaluate a confusion matrix. This helpful module implements a large number of them

  • q1
  • q2
  • q3
  • q4
  • q5
  • q6
  • q7
  • dpower
  • agf
  • markedness
  • bcr
  • ber
  • gm
  • agm
  • op
  • req
  • tanimoto
  • roc
  • specificity
  • fprate
  • fnrate
  • precision
  • negativepv
  • plr
  • nlr
  • youden
  • accuracy
  • fscore
  • f2measure
  • fmeasure
  • f0_5measure
  • power
  • logpower
  • bajic_k
  • chisquare
  • ctg
  • yuleY
  • yuleQ
  • ivesgibbs
  • acp
  • acc
  • gdip1
  • gdip2
  • gdip3
  • hamming
  • jaccard

The original impelmentation was in Perl around 2005 and I appear to have not noted many of the references. My apologies.

Details of the calcualtion are in the docstring. This module should be used as follows:

from metrics import Metrics

Metrics.list_metrics() # lists method names

Metrics.list_metrics(verbose=True) # gives a dictionary with the docstring

Metrics.measure(method, tp=TP, fp=FP, tn=TN, fn=FN) # for True Positive, False Negative etc.

You probably want to wrap this with try .. except as it will show an error if inappropriate data is given. The measure method will convert counts to proportional data.

Don’t forget to Metrics.cite(method) which will give a list of citations, if available. If you wish to add to the citations then submit a pull request.

I’d like to expand the help text in due course for each metric.

Further information on many of the metrics and their behaviour can be found at (Tharwat, Applied Computing and Informatics (2018),https://doi.org/10.1016/j.aci.2018.08.003)[https://doi.org/10.1016/j.aci.2018.08.003]

[Find this on BitBucket]( https://bitbucket.org/davidmam/metrics.git)

q1 q2 q3 q4 q5 q6 q7 dpower agf markedness bcr ber gm agm op req tanimoto roc specificity fprate fnrate precision negativepv plr nlr youden accuracy fscore f2measure fmeasure f0_5measure power logpower bajic_k chisquare ctg yuleY yuleQ ivesgibbs acp acc gdip1 gdip2 gdip3 hamming jaccard

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

confusion-metrics-0.1.0.tar.gz (8.2 kB view hashes)

Uploaded source

Built Distribution

confusion_metrics-0.1.0-py3-none-any.whl (10.4 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page