mergernet.visualization.plot#

Functions

`color_color`(table_e, table_s[, xlim, ylim, ...])
`conf_matrix`(y_true, y_pred[, one_hot, labels])
`data_distribution`(train_series, test_series, ...)
`mag_class_distribution`(df, n_folds, n_bins, ...)
`object_position`(ra, dec, sample, color_map)
`roc`(true_series, pred_series, curves[, ...])	Plot ROC curve of a model :param true_series: The series of true labels. Shape of each serie: (n, n_class), where rep is the number of repetitions (std curve computation), n is the number of examples and n_class is the number of classes. :type true_series: list of Serie :param pred_series: The series of predictions. Shape of each serie: (n, n_class), where rep is the number of repetition (std curve computation), n is the number of examples and n_class is the number of classes. :type pred_series: list of Serie :param curves: Values can include: 0 ... n_class, "macro" or "micro". If curve is a int, then a curve of the specific class will be ploted. If curve is "macro", the macro-average of all classes will be computed. If curve is "micro", the micro-average of all classes will be computed. :type curves: list.
`train_metrics`(mean_series, error_series[, ...])

Classes