Multivariate Pattern Analysis in Python |
Inheritance diagram for mvpa.clfs.transerror:
Utility class to compute the transfer error of classifiers.
Bases: mvpa.misc.state.ClassWithCollections
Compute (or return) some error of a (trained) classifier on a dataset.
See also
Please refer to the documentation of the base class for more information:
Note
Available state variables:
(States enabled by default are listed with +)
See also
Please refer to the documentation of the base class for more information:
Initialization.
Parameters: |
|
---|
TODO Think that labels might be also symbolic thus can’t directly be indicies of the array
Bases: mvpa.clfs.transerror.ClassifierError
For a given classifier report an error based on internally computed error measure (given by some ConfusionMatrix stored in some state variable of Classifier).
This way we can perform feature selection taking as the error criterion either learning error, or transfer to splits error in the case of SplitClassifier
Note
Available state variables:
(States enabled by default are listed with +)
Initialization.
Parameters: |
|
---|
Bases: mvpa.clfs.transerror.SummaryStatistics
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 asstring(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: |
|
---|
‘Pretty print’ the matrix
Parameters: |
|
---|
Return a list of separate confusion matrix per each stored set
Provide presentation of confusion matrix in image
Parameters: |
|
---|---|
Return type: | (fig, im, cb) – figure, imshow, colorbar |
Bases: object
Generic class for ROC curve computation and plotting
Parameters: |
|
---|
Compute and return set of AUC values 1 per label
Bases: mvpa.clfs.transerror.SummaryStatistics
Class to contain information and display on regression results.
Initialize RegressionStatistics
Parameters: |
|
---|
‘Pretty print’ the statistics
Provide presentation of regression performance in image
Parameters: |
|
---|---|
Return type: | (fig, im, cb) – figure, imshow, colorbar |
Bases: object
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.
Initialize SummaryStatistics
targets or predictions cannot be provided alone (ie targets without predictions)
Parameters: |
|
---|
Add new results to the set of known results
‘Pretty print’ the matrix
Parameters: |
|
---|
Actually compute the confusion matrix based on all the sets
Cleans summary – all data/sets are wiped out
Return a list of separate summaries per each stored set
Bases: mvpa.clfs.transerror.ClassifierError
Compute the transfer error of a (trained) classifier on a dataset.
The actual error value is computed using a customizable error function. Optionally the classifier can be trained by passing an additional training dataset to the __call__() method.
Note
Available state variables:
(States enabled by default are listed with +)
Initialization.
Parameters: |
|
---|