mvpa2.clfs.base.ConfusionMatrix

class mvpa2.clfs.base.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.

Attributes

error
labels
labels_map
matrices	Return a list of separate confusion matrix per each stored set
matrix
percent_correct
sets
stats
summaries	Return a list of separate summaries per each stored set

Methods

__call__(predictions, targets[, estimates, ...])	Computes confusion matrix (counts)
add(targets, predictions[, estimates])	Add new results to the set of known results
as_string([short, header, summary, description])	‘Pretty print’ the matrix
compute()	Actually compute the confusion matrix based on all the sets
get_labels_map()
plot([labels, numbers, origin, ...])	Provide presentation of confusion matrix in image
reset()	Cleans summary – all data/sets are wiped out
set_labels_map(val)

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

Attributes

error
labels
labels_map
matrices	Return a list of separate confusion matrix per each stored set
matrix
percent_correct
sets
stats
summaries	Return a list of separate summaries per each stored set

Methods

__call__(predictions, targets[, estimates, ...])	Computes confusion matrix (counts)
add(targets, predictions[, estimates])	Add new results to the set of known results
as_string([short, header, summary, description])	‘Pretty print’ the matrix
compute()	Actually compute the confusion matrix based on all the sets
get_labels_map()
plot([labels, numbers, origin, ...])	Provide presentation of confusion matrix in image
reset()	Cleans summary – all data/sets are wiped out
set_labels_map(val)

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=None, **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)