mvpa2.clfs.transerrorΒΆ
Utility class to compute the transfer error of classifiers.
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 The Friedman test tests the null hypothesis that repeated measurements of the same individuals have the same distribution. |
| linregress(x[, y]) | Calculate a regression line This computes a 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(x[, axis]) | Compute the mean over the given 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 :Parameters: table : list of lists of strings What is aimed to be printed out : None or stream Where to print. |
Classes
| BayesConfusionHypothesis([alpha, ...]) | Bayesian hypothesis testing on confusion matrices. |
| ClassWithCollections([descr]) | Base class for objects which contain any known collection Classes inherited from this class gain ability to access collections and their items as simple attributes. |
| 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) This class is very similar to ConfusionMatrix and ConfusionMatrixError. |
| 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” This class can be used to compute confusion matrices from classifier output inside cross-validation fold without the stats conditional attribute. |
| Dataset(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes. |
| Node([space, 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 It takes care about collecting the sets, which are just tuples (targets, predictions, estimates). |
Exceptions
| BayesConfusionHypothesis([alpha, ...]) | Bayesian hypothesis testing on confusion matrices. |
| ClassWithCollections([descr]) | Base class for objects which contain any known collection Classes inherited from this class gain ability to access collections and their items as simple attributes. |
| 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) This class is very similar to ConfusionMatrix and ConfusionMatrixError. |
| 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” This class can be used to compute confusion matrices from classifier output inside cross-validation fold without the stats conditional attribute. |
| Dataset(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes. |
| Node([space, 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 It takes care about collecting the sets, which are just tuples (targets, predictions, estimates). |
