mvpa2.clfs.transerrorΒΆ

Utility class to compute the transfer error of classifiers.

Inheritance diagram of mvpa2.clfs.transerror

Functions

auc_error(predicted, target) Computes the area under the ROC for the given the target and predicted to make the prediction.
auto_null_dist(dist) Cheater for human beings – wraps dist if needed with some
ceil(x) Return the ceiling of x as a float.
chisquare(obs[, exp]) Compute the chisquare value of a contingency table with arbitrary dimensions.
corr_error(predicted, target) Computes the correlation between the target and the predicted values.
corr_error_prob(predicted, target) Computes p-value of correlation between the target and the predicted values.
enhanced_doc_string(item, \*args, \*\*kwargs) Generate enhanced doc strings for various items.
friedmanchisquare(\*args) Computes the Friedman test for repeated measurements
linregress(x[, y]) Calculate a linear least-squares regression for two sets of measurements.
log10(x) Return the base 10 logarithm of x.
mean_mismatch_error(predicted, target) Computes the percentage of mismatches between some target and some predicted values.
mean_power_fx(data) Returns mean power
nanmean(a[, axis, dtype, out, keepdims]) Compute the arithmetic mean along the specified axis, ignoring NaNs.
relative_rms_error(predicted, target) Ratio between RMSE and root mean power of target output.
rms_error(predicted, target) Computes the root mean squared error of some target and some predicted values.
root_mean_power_fx(data) Returns root mean power
table2string(table[, out]) Given list of lists figure out their common widths and print to out

Classes

BayesConfusionHypothesis([alpha, ...]) Bayesian hypothesis testing on confusion matrices.
ClassWithCollections([descr]) Base class for objects which contain any known collection
ClassifierError(clf[, labels, train]) Compute (or return) some error of a (trained) classifier on a dataset.
Collectable([value, name, doc]) Collection element.
ConditionalAttribute([enabled]) Simple container intended to conditionally store the value
Confusion([attr, labels, add_confusion_obj]) Compute a confusion matrix from predictions and targets (Node interface)
ConfusionBasedError(clf[, labels, ...]) For a given classifier report an error based on internally computed error measure (given by some ConfusionMatrix stored in some conditional attribute of Classifier).
ConfusionMatrix([labels, labels_map]) Class to contain information and display confusion matrix.
ConfusionMatrixError([labels]) Compute confusion matrix as an “error function”
Dataset(samples[, sa, fa, a]) Generic storage class for datasets with multiple attributes.
Doi(doi[, key])

Attributes

Node([space, pass_attr, postproc]) Common processing object.
ROCCurve(labels[, sets]) Generic class for ROC curve computation and plotting
RegressionStatistics(\*\*kwargs) Class to contain information and display on regression results.
StringIO([buf]) class StringIO([buffer])
SummaryStatistics([targets, predictions, ...]) Basic class to collect targets/predictions and report summary statistics

Exceptions

BayesConfusionHypothesis([alpha, ...]) Bayesian hypothesis testing on confusion matrices.
ClassWithCollections([descr]) Base class for objects which contain any known collection
ClassifierError(clf[, labels, train]) Compute (or return) some error of a (trained) classifier on a dataset.
Collectable([value, name, doc]) Collection element.
ConditionalAttribute([enabled]) Simple container intended to conditionally store the value
Confusion([attr, labels, add_confusion_obj]) Compute a confusion matrix from predictions and targets (Node interface)
ConfusionBasedError(clf[, labels, ...]) For a given classifier report an error based on internally computed error measure (given by some ConfusionMatrix stored in some conditional attribute of Classifier).
ConfusionMatrix([labels, labels_map]) Class to contain information and display confusion matrix.
ConfusionMatrixError([labels]) Compute confusion matrix as an “error function”
Dataset(samples[, sa, fa, a]) Generic storage class for datasets with multiple attributes.
Doi(doi[, key])

Attributes

Node([space, pass_attr, postproc]) Common processing object.
ROCCurve(labels[, sets]) Generic class for ROC curve computation and plotting
RegressionStatistics(\*\*kwargs) Class to contain information and display on regression results.
StringIO([buf]) class StringIO([buffer])
SummaryStatistics([targets, predictions, ...]) Basic class to collect targets/predictions and report summary statistics