68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
import numpy as np
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import torch
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from sklearn.ensemble import IsolationForest
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from sklearn.metrics import recall_score, roc_auc_score, average_precision_score
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from ml_lib.metrics._base_score import _BaseScores
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from ml_lib.utils.tools import to_one_hot
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class BinaryScores(_BaseScores):
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def __init__(self, *args):
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super(BinaryScores, self).__init__(*args)
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def __call__(self, outputs):
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summary_dict = dict()
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# Additional Score like the unweighted Average Recall:
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#########################
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# INIT
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if isinstance(outputs['batch_y'], torch.Tensor):
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y_true = outputs['batch_y'].cpu().numpy()
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else:
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y_true = torch.cat([output['batch_y'] for output in outputs]).cpu().numpy()
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if isinstance(outputs['y'], torch.Tensor):
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y_pred = outputs['y'].cpu().numpy()
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else:
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y_pred = torch.cat([output['y'] for output in outputs]).squeeze().cpu().float().numpy()
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# UnweightedAverageRecall
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# y_true = torch.cat([output['batch_y'] for output in outputs]).cpu().numpy()
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# y_pred = torch.cat([output['element_wise_recon_error'] for output in outputs]).squeeze().cpu().numpy()
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# How to apply a threshold manualy
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# y_pred = (y_pred >= 0.5).astype(np.float32)
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# How to apply a threshold by IF (Isolation Forest)
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clf = IsolationForest()
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y_score = clf.fit_predict(y_pred.reshape(-1, 1))
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y_score = (np.asarray(y_score) == -1).astype(np.float32)
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uar_score = recall_score(y_true, y_score, labels=[0, 1], average='macro',
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sample_weight=None, zero_division='warn')
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summary_dict.update(dict(uar_score=uar_score))
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#########################
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# Precission
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precision_score = average_precision_score(y_true, y_score)
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summary_dict.update(dict(precision_score=precision_score))
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#########################
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# AUC
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try:
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auc_score = roc_auc_score(y_true=y_true, y_score=y_score)
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summary_dict.update(dict(auc_score=auc_score))
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except ValueError:
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summary_dict.update(dict(auc_score=-1))
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#########################
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# pAUC
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try:
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pauc = roc_auc_score(y_true=y_true, y_score=y_score, max_fpr=0.15)
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summary_dict.update(dict(pauc_score=pauc))
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except ValueError:
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summary_dict.update(dict(pauc_score=-1))
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return summary_dict |