torchdrug.metrics#
Basic Metrics#
AUROC#
- area_under_roc(pred, target)[source]#
Area under receiver operating characteristic curve (ROC).
- Parameters
pred (Tensor) – predictions of shape \((n,)\)
target (Tensor) – binary targets of shape \((n,)\)
- AUROC()#
alias of
torchdrug.metrics.area_under_roc
AUPRC#
- area_under_prc(pred, target)[source]#
Area under precision-recall curve (PRC).
- Parameters
pred (Tensor) – predictions of shape \((n,)\)
target (Tensor) – binary targets of shape \((n,)\)
- AUPRC()#
alias of
torchdrug.metrics.area_under_prc
R2#
Accuracy#
Matthews Correlation Coefficient#
- matthews_corrcoef(pred, target)[source]#
Matthews correlation coefficient between prediction and target.
Definition follows matthews_corrcoef for K classes in sklearn. For details, see: https://scikit-learn.org/stable/modules/model_evaluation.html#matthews-corrcoef
- Parameters
pred (Tensor) – prediction of shape :math: (N, K)
target (Tensor) – target of shape :math: (N,)
Pearson Correlation Coefficient#
Spearman’s Rank Correlation Coefficient#
Variadic Accuracy#
- variadic_accuracy(input, target, size)[source]#
Classification accuracy for categories with variadic sizes.
Suppose there are \(N\) samples, and the number of categories in all samples is summed to \(B\).
- Parameters
input (Tensor) – prediction of shape \((B,)\)
target (Tensor) – target of shape \((N,)\). Each target is a relative index in a sample.
size (Tensor) – number of categories of shape \((N,)\)
Variadic Area Under ROC#
- variadic_area_under_roc(pred, target, size)[source]#
Area under receiver operating characteristic curve (ROC) for sets with variadic sizes.
Suppose there are \(N\) sets, and the sizes of all sets are summed to \(B\).
- Parameters
pred (Tensor) – prediction of shape \((B,)\)
target (Tensor) – target of shape \((B,)\).
size (Tensor) – size of sets of shape \((N,)\)
Variadic Area Under PRC#
- variadic_area_under_prc(pred, target, size)[source]#
Area under precision-recall curve (PRC) for sets with variadic sizes.
Suppose there are \(N\) sets, and the sizes of all sets are summed to \(B\).
- Parameters
pred (Tensor) – prediction of shape \((B,)\)
target (Tensor) – target of shape \((B,)\).
size (Tensor) – size of sets of shape \((N,)\)
Variadic Top Precision#
- variadic_top_precision(pred, target, size, k)[source]#
Top-k precision for sets with variadic sizes.
Suppose there are \(N\) sets, and the sizes of all sets are summed to \(B\).
- Parameters
pred (Tensor) – prediction of shape \((B,)\)
target (Tensor) – target of shape \((B,)\)
size (Tensor) – size of sets of shape \((N,)\)
k (LongTensor) – the k in “top-k” for different sets of shape \((N,)\)
F1 Max#
- f1_max(pred, target)[source]#
F1 score with the optimal threshold.
This function first enumerates all possible thresholds for deciding positive and negative samples, and then pick the threshold with the maximal F1 score.
- Parameters
pred (Tensor) – predictions of shape \((B, N)\)
target (Tensor) – binary targets of shape \((B, N)\)
Chemical Metrics#
SA#
- SA(pred)[source]#
Synthetic accesibility score.
- Parameters
pred (PackedMolecule) – molecules to evaluate
QED#
- QED(pred)[source]#
Quantitative estimation of drug-likeness.
- Parameters
pred (PackedMolecule) – molecules to evaluate
Chemical Validity#
- chemical_validity(pred)[source]#
Chemical validity of molecules.
- Parameters
pred (PackedMolecule) – molecules to evaluate
LogP#
- logP(pred)[source]#
Logarithm of partition coefficient between octanol and water for a compound.
- Parameters
pred (PackedMolecule) – molecules to evaluate
Penalized LogP#
- penalized_logP(pred)[source]#
Logarithm of partition coefficient, penalized by cycle length and synthetic accessibility.
- Parameters
pred (PackedMolecule) – molecules to evaluate