Molenet dataset

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How Missing Labels Are Handled in HiMol Finetuning

HiMol uses a three-valued label encoding for classification: +1 (positive/active), -1 (negative/inactive), and 0 (missing/unmeasured). The 0 sentinel is established during data loading in finetune/loader.py — DeepChem weights of 0 and NaN values are both mapped to 0, while real 0/1 labels are converted to -1/+1.

Loss Computation

In finetune/finetune.py (train() and eval() functions), the key mechanism is:

is_valid = y**2 > 0                          # 0²=0 → invalid; (±1)²=1 → valid
loss_mat = criterion(pred.double(), (y+1)/2)  # {-1,+1} → {0,1} for BCE
loss_mat = torch.where(is_valid, loss_mat, torch.zeros(...))  # zero out missing
loss = torch.sum(loss_mat) / torch.sum(is_valid)              # normalize by valid count

Missing labels are zeroed out in the loss matrix and excluded from the normalization denominator (torch.sum(is_valid)), so they contribute nothing to the gradient. The same pattern appears in finetune/optimization.py.

Metrics Computation

• ROC-AUC (per-task): Only computed for tasks with both positive and negative examples. Within each task, the is_valid mask filters out missing entries before passing to roc_auc_score. Tasks that can't be computed are skipped, and a "Some target is missing!" warning with the missing ratio is printed.

• Macro F1 (finetune/metrics.py): Classes with zero positive labels are excluded from the macro average via a mask. Classes with positive labels but no positive predictions get NaN precision, which is converted to 0.

• Micro F1: Uses torchmetrics' MultilabelF1Score (includes all labels in averaging).

• Regression: No missing label handling — MSE/MAE/RMSE computed directly on all predictions.

Note: The eval() function has a bug where loss is computed using only the last batch's batch.y rather than the full concatenated dataset. The eval_chebi() function fixes this by using the full y_true array.

[aditya0by0]

How Missing Labels Are Handled in HiMol Finetuning

HiMol uses a three-valued label encoding for classification: +1 (positive/active), -1 (negative/inactive), and 0 (missing/unmeasured). The 0 sentinel is established during data loading in finetune/loader.py — DeepChem weights of 0 and NaN values are both mapped to 0, while real 0/1 labels are converted to -1/+1.

Loss Computation

In finetune/finetune.py (train() and eval() functions), the key mechanism is:

is_valid = y**2 > 0                          # 0²=0 → invalid; (±1)²=1 → valid
loss_mat = criterion(pred.double(), (y+1)/2)  # {-1,+1} → {0,1} for BCE
loss_mat = torch.where(is_valid, loss_mat, torch.zeros(...))  # zero out missing
loss = torch.sum(loss_mat) / torch.sum(is_valid)              # normalize by valid count

Missing labels are zeroed out in the loss matrix and excluded from the normalization denominator (torch.sum(is_valid)), so they contribute nothing to the gradient. The same pattern appears in finetune/optimization.py.

Metrics Computation

• ROC-AUC (per-task): Only computed for tasks with both positive and negative examples. Within each task, the is_valid mask filters out missing entries before passing to roc_auc_score. Tasks that can't be computed are skipped, and a "Some target is missing!" warning with the missing ratio is printed.
• Macro F1 (finetune/metrics.py): Classes with zero positive labels are excluded from the macro average via a mask. Classes with positive labels but no positive predictions get NaN precision, which is converted to 0.
• Micro F1: Uses torchmetrics' MultilabelF1Score (includes all labels in averaging).
• Regression: No missing label handling — MSE/MAE/RMSE computed directly on all predictions.

Note: The eval() function has a bug where loss is computed using only the last batch's batch.y rather than the full concatenated dataset. The eval_chebi() function fixes this by using the full y_true array.

fixed in ChEB-AI/python-chebai#174