Metadata-Version: 1.2
Name: confusion-metrics
Version: 0.1.0
Summary: A collection of metrics for analysing confusion matrices
Home-page: https://bitbucket.org/davidmam/metrics.git
Author: Dr David Martin
Author-email: d.m.a.martin@dundee.ac.uk
License: UNKNOWN
Description-Content-Type: text/markdown
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
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.0
