Metadata-Version: 1.2
Name: confusion-metrics
Version: 0.0.4
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
        
            * acc 
            * accuracy 
            * acp 
            * bajic_k 
            * chisquare 
            * ctg 
            * f2measure 
            * fmeasure 
            * fprate 
            * fscore 
            * gdip1 
            * gdip2 
            * gdip3 
            * ivesgibbs 
            * list_metrics 
            * logpower 
            * power 
            * precision 
            * q1 
            * q2 (True Positive rate, recall, sensitivity)
            * q3 
            * q4 
            * q5 
            * q6 
            * q7  (Matthews Correlation Coefficient)
            * req (relative Error Quotient)
            * roc 
            * specificity 
            * tanimoto (Tanimoto Index)
            * yule
            * hamming (Hamming distance as a proportion)
            * 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.
        
        [Find this on BitBucket]( https://bitbucket.org/davidmam/metrics.git)
        
            
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.0
