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PyMVPA: Python MultiVariate Pattern Analysis
Package mvpa :: Package clfs :: Module transerror :: Class SummaryStatistics
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Class SummaryStatistics

source code

Basic class to collect targets/predictions and report summary statistics

It takes care about collecting the sets, which are just tuples (targets, predictions, values). While 'computing' the matrix, all sets are considered together. Children of the class are responsible for computation and display.

Instance Methods [hide private]
 
__init__(self, targets=None, predictions=None, values=None, sets=None)
Initialize SummaryStatistics		 source code
 
add(self, targets, predictions, values=None)
Add new results to the set of known results		 source code
 
asstring(self, short=False, header=True, summary=True, description=False)
'Pretty print' the matrix		 source code
 
__str__(self)
String summary over the SummaryStatistics		 source code
 
__iadd__(self, other)
Add the sets from other s SummaryStatistics to current one		 source code
 
__add__(self, other)
Add two `SummaryStatistics`s		 source code
 
compute(self)
Actually compute the confusion matrix based on all the sets		 source code
 
_compute(self)
Compute basic statistics		 source code
 
summaries(self)
Return a list of separate summaries per each stored set		 source code
 
error(self) source code
 
stats(self) source code
 
reset(self)
Cleans summary -- all data/sets are wiped out		 source code

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __subclasshook__

Class Variables [hide private]
  _STATS_DESCRIPTION = '# of sets', 'number of target/prediction...
  sets = property(lambda self: self.__sets)
Instance Variables [hide private]
  _computed
Flag either it was computed for a given set of data
  __sets
Datasets (target, prediction) to compute confusion matrix on
  _stats
Dictionary to keep statistics. Initialized here to please pylint
Properties [hide private]

Inherited from object: __class__

Method Details [hide private]

__init__(self, targets=None, predictions=None, values=None, sets=None)
(Constructor)

source code 

Initialize SummaryStatistics

targets or predictions cannot be provided alone (ie targets without predictions)

Parameters:
  • targets - Optional set of targets
  • predictions - Optional set of predictions
  • values - Optional set of values (which served for prediction)
  • sets - Optional list of sets
Overrides: object.__init__

asstring(self, short=False, header=True, summary=True, description=False)

source code 
'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

__str__(self)
(Informal representation operator)

source code 

String summary over the SummaryStatistics

It would print description of the summary statistics if 'CM' debug target is active

Overrides: object.__str__

summaries(self)

source code 
Return a list of separate summaries per each stored set
Decorators:
  • @property

error(self)

source code 
Decorators:
  • @property

stats(self)

source code 
Decorators:
  • @property

Class Variable Details [hide private]

_STATS_DESCRIPTION

Value:
'# of sets', 'number of target/prediction sets which were provided', N\
one,

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PyMVPA: Python MultiVariate Pattern Analysis
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