Metrics (rojak.turbulence.metrics)

Functions

accuracy([truth, prediction, confuse_matrix])	Compute the Accuracy (ACC)
area_under_curve(x_values, y_values)	Area under the curve
binary_classification_curve(sorted_truth, ...)	Binary classification curve
binary_classification_rate_from_cumsum(...)	Binary classification curve with cumulative sum on labels
confusion_matrix(truth, prediction)	Compute the confusion matrix
contingency_table(first_var, second_var, ...)	Contingency Table for multidimensional arrays
critical_success_index([truth, prediction, ...])	Compute the Critical Success Index (CSI) or the Jaccard Similarity Coefficient Score
f1_score([truth, prediction, confuse_matrix])	Compute the F1 Score
gilbert_skill_score([truth, prediction, ...])	Compute the Gilbt Skill Score
jaccard_index_multidim(first_var, ...)	Jaccard Index or Critical Success Index for multidimensional data
matthews_corr_coeff([truth, prediction, ...])	Compute the Matthew's Correlation Coefficient
matthews_corr_coeff_multidim(first_var, ...)	Matthews Correlation Coefficient for multidimensional arrays
mean_absolute_error(truth, prediction)	Mean Absolute Error (MAE)
received_operating_characteristic(...[, ...])	Received operating characteristic or ROC curve
sensitivity(true_positive, false_negative)	Sensitivity statistical metric
specificity(true_negative, false_positive)	Specificity statistical metric :param true_negative: :param false_positive:
true_skill_score([truth, prediction, ...])	True Skill Score (TSS) statistic

Classes

BinaryClassificationRateFromLabels(...)	Create new instance of BinaryClassificationRateFromLabels(true_positives_rate, false_positives_rate)
BinaryClassificationResult(false_positives, ...)	Create new instance of BinaryClassificationResult(false_positives, true_positives, thresholds)
ContingencyTable(n_00, n_11, n_01, n_10)	Create new instance of ContingencyTable(n_00, n_11, n_01, n_10)