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import torch |
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import torch.nn.functional as F |
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from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, classification_report, precision_score, recall_score |
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import numpy as np |
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import logging |
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logger = logging.getLogger(__name__) |
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class GraphMetrics: |
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"""Comprehensive evaluation metrics for graph learning""" |
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@staticmethod |
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def accuracy(pred, target): |
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"""Classification accuracy with validation""" |
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try: |
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if pred.dim() > 1: |
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pred_labels = pred.argmax(dim=1) |
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else: |
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pred_labels = pred |
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if pred_labels.shape != target.shape: |
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raise ValueError("Prediction and target shapes don't match") |
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correct = (pred_labels == target).float() |
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accuracy = correct.mean().item() |
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if torch.isnan(torch.tensor(accuracy)) or torch.isinf(torch.tensor(accuracy)): |
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logger.warning("Invalid accuracy computed, returning 0.0") |
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return 0.0 |
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return accuracy |
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except Exception as e: |
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logger.error(f"Accuracy computation failed: {e}") |
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return 0.0 |
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@staticmethod |
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def f1_score_macro(pred, target): |
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"""Macro F1 score with robust error handling""" |
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try: |
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if pred.dim() > 1: |
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pred_labels = pred.argmax(dim=1) |
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else: |
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pred_labels = pred |
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pred_labels = pred_labels.cpu().numpy() |
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target_labels = target.cpu().numpy() |
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if len(pred_labels) == 0 or len(target_labels) == 0: |
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return 0.0 |
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f1 = f1_score(target_labels, pred_labels, average='macro', zero_division=0) |
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if np.isnan(f1) or np.isinf(f1): |
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logger.warning("Invalid F1 macro score, returning 0.0") |
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return 0.0 |
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return float(f1) |
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except Exception as e: |
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logger.error(f"F1 macro computation failed: {e}") |
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return 0.0 |
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@staticmethod |
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def f1_score_micro(pred, target): |
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"""Micro F1 score with robust error handling""" |
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try: |
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if pred.dim() > 1: |
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pred_labels = pred.argmax(dim=1) |
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else: |
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pred_labels = pred |
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pred_labels = pred_labels.cpu().numpy() |
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target_labels = target.cpu().numpy() |
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if len(pred_labels) == 0 or len(target_labels) == 0: |
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return 0.0 |
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f1 = f1_score(target_labels, pred_labels, average='micro', zero_division=0) |
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if np.isnan(f1) or np.isinf(f1): |
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logger.warning("Invalid F1 micro score, returning 0.0") |
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return 0.0 |
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return float(f1) |
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except Exception as e: |
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logger.error(f"F1 micro computation failed: {e}") |
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return 0.0 |
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@staticmethod |
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def precision_recall(pred, target, average='macro'): |
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"""Compute precision and recall scores""" |
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try: |
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if pred.dim() > 1: |
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pred_labels = pred.argmax(dim=1) |
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else: |
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pred_labels = pred |
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pred_labels = pred_labels.cpu().numpy() |
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target_labels = target.cpu().numpy() |
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if len(pred_labels) == 0 or len(target_labels) == 0: |
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return 0.0, 0.0 |
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precision = precision_score(target_labels, pred_labels, average=average, zero_division=0) |
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recall = recall_score(target_labels, pred_labels, average=average, zero_division=0) |
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if np.isnan(precision) or np.isinf(precision): |
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precision = 0.0 |
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if np.isnan(recall) or np.isinf(recall): |
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recall = 0.0 |
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return float(precision), float(recall) |
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except Exception as e: |
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logger.error(f"Precision/recall computation failed: {e}") |
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return 0.0, 0.0 |
