mvpa2.clfs.transerrorΒΆ
Utility class to compute the transfer error of classifiers.
Functions
Doi(\*args, \*\*kwargs) |
Perform no good and no bad |
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. |
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. |
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 |
