immuneML.ml_metrics package¶
Submodules¶
immuneML.ml_metrics.ClassificationMetric module¶
- class immuneML.ml_metrics.ClassificationMetric.ClassificationMetric(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Bases:
Enum
- ACCURACY = 'accuracy_score'¶
- AUC = 'roc_auc_score'¶
- BALANCED_ACCURACY = 'balanced_accuracy_score'¶
- CONFUSION_MATRIX = 'confusion_matrix'¶
- F1_MACRO = 'f1_score_macro'¶
- F1_MICRO = 'f1_score_micro'¶
- F1_WEIGHTED = 'f1_score_weighted'¶
- LOG_LOSS = 'log_loss'¶
- PRECISION = 'precision_score'¶
- RECALL = 'recall_score'¶
immuneML.ml_metrics.ClusteringMetric module¶
immuneML.ml_metrics.MetricUtil module¶
- class immuneML.ml_metrics.MetricUtil.MetricUtil[source]¶
Bases:
object
- static get_metric_fn(metric: ClassificationMetric)[source]¶
- static score_for_metric(metric: ClassificationMetric, predicted_y, predicted_proba_y, true_y, classes)[source]¶
Note: when providing label classes, make sure the ‘positive class’ is sorted last. This sorting should be done automatically when accessing Label.values