mvpa.clfs.transerror.ConfusionMatrix¶
- class mvpa.clfs.transerror.ConfusionMatrix(labels=None, labels_map=None, **kwargs)¶
Class to contain information and display confusion matrix.
Implementation of the SummaryStatistics in the case of classification problem. Actual computation of confusion matrix is delayed until all data is acquired (to figure out complete set of labels). If testing data doesn’t have a complete set of labels, but you like to include all labels, provide them as a parameter to the constructor.
Confusion matrix provides a set of performance statistics (use as_string(description=True) for the description of abbreviations), as well ROC curve (http://en.wikipedia.org/wiki/ROC_curve) plotting and analysis (AUC) in the limited set of problems: binary, multiclass 1-vs-all.
Initialize ConfusionMatrix with optional list of labels
Parameters : labels : list
Optional set of labels to include in the matrix
labels_map : None or dict
Dictionary from original dataset to show mapping into numerical labels
targets :
Optional set of targets
predictions :
Optional set of predictions
- as_string(short=False, header=True, summary=True, description=False)¶
‘Pretty print’ the matrix
Parameters : short : bool
if True, ignores the rest of the parameters and provides consise 1 line summary
header : bool
print header of the table
summary : bool
print summary (accuracy)
description : bool
print verbose description of presented statistics
- error¶
- get_labels_map()¶
- labels¶
- labels_map¶
- matrices¶
Return a list of separate confusion matrix per each stored set
- matrix¶
- percent_correct¶
- plot(labels=None, numbers=False, origin='upper', numbers_alpha=None, xlabels_vertical=True, numbers_kwargs={}, **kwargs)¶
Provide presentation of confusion matrix in image
Parameters : labels : list of int or str
Optionally provided labels guarantee the order of presentation. Also value of None places empty column/row, thus provides visual groupping of labels (Thanks Ingo)
numbers : bool
Place values inside of confusion matrix elements
numbers_alpha : None or float
Controls textual output of numbers. If None – all numbers are plotted in the same intensity. If some float – it controls alpha level – higher value would give higher contrast. (good value is 2)
origin : str
Which left corner diagonal should start
xlabels_vertical : bool
Either to plot xlabels vertical (benefitial if number of labels is large)
numbers_kwargs : dict
Additional keyword parameters to be added to numbers (if numbers is True)
**kwargs :
Additional arguments given to imshow (eg me cmap)
Returns : (fig, im, cb) – figure, imshow, colorbar :
- set_labels_map(val)¶
