def __init__(self,
average: Union[Average, str] = Average.MACRO,
output_transform: Callable = lambda x: x) -> None:
metric_fn = ROCAUCMetric(average=Average(average))
super().__init__(metric_fn=metric_fn,
output_transform=output_transform,
save_details=False)
def _compute_fn(pred, label):
return compute_roc_auc(
y_pred=pred,
y=label,
to_onehot_y=to_onehot_y,
softmax=softmax,
other_act=other_act,
average=Average(average),
)
def __init__(
self,
to_onehot_y: bool = False,
softmax: bool = False,
average: Union[Average, str] = Average.MACRO,
output_transform: Callable = lambda x: x,
device: Optional[Union[str, torch.device]] = None,
) -> None:
super().__init__(output_transform, device=device)
self.to_onehot_y = to_onehot_y
self.softmax = softmax
self.average: Average = Average(average)
def __init__(
self,
to_onehot_y: bool = False,
softmax: bool = False,
other_act: Optional[Callable] = None,
average: Union[Average, str] = Average.MACRO,
output_transform: Callable = lambda x: x,
) -> None:
super().__init__(output_transform)
self.to_onehot_y = to_onehot_y
self.softmax = softmax
self.other_act = other_act
self.average: Average = Average(average)
def __init__(
self,
to_onehot_y: bool = False,
activation: Optional[Union[str, Callable]] = None,
bin_mode: Optional[str] = "threshold",
bin_threshold: Union[float, Sequence[float]] = 0.5,
metric_name: str = "hit_rate",
average: Union[Average, str] = Average.MACRO,
zero_division: int = 0,
output_transform: Callable = lambda x: x,
device: Optional[torch.device] = None,
) -> None:
super().__init__(output_transform, device=device)
self.to_onehot_y = to_onehot_y
self.activation = activation
self.bin_mode = bin_mode
self.bin_threshold = bin_threshold
self.metric_name = metric_name
self.average: Average = Average(average)
self.zero_division = zero_division
def do_calculate_metric(
confusion_ele_list: List[np.ndarray],
metric_name: str,
average: Union[Average, str] = "none",
zero_division: int = 0,
):
"""
Args:
confusion_ele_list: the returned result of function ``cal_confusion_matrix_elements``.
metric_name: the simplified metric name from function ``check_metric_name_and_unify``.
average: type of averaging performed if not binary classification.
Defaults to ``"macro"``.
- ``"macro"``: calculate metrics for each label, and find their unweighted mean.
This does not take label imbalance into account.
- ``"weighted"``: calculate metrics for each label, and find their average
weighted by support (the number of true instances for each label).
- ``"micro"``: calculate metrics globally by considering each element of the label
indicator matrix as a label.
- ``"none"``: the scores for each class are returned.
zero_division: the value to return when there is a zero division, for example, when all
predictions and labels are negative. Defaults to 0.
"""
ele_list: List[Union[np.ndarray, int, float]]
metric = metric_name
div_0 = zero_division
# pre-process average
average = Average(average)
bin_flag: bool
if len(confusion_ele_list[0].shape) == 0:
bin_flag = True
average = Average.NONE # for binary tasks, other average methods are meaningless.
ele_list = [int(l) for l in confusion_ele_list]
else:
bin_flag = False
if average == Average.MICRO:
ele_list = [int(l.sum()) for l in confusion_ele_list]
else:
ele_list = confusion_ele_list
tp, tn, fp, fn, p, n = ele_list
# calculate
numerator: Union[np.ndarray, int, float]
denominator: Union[np.ndarray, int, float]
if metric == "tpr":
numerator, denominator = tp, p
elif metric == "tnr":
numerator, denominator = tn, n
elif metric == "ppv":
numerator, denominator = tp, (tp + fp)
elif metric == "npv":
numerator, denominator = tn, (tn + fn)
elif metric == "fnr":
numerator, denominator = fn, p
elif metric == "fpr":
numerator, denominator = fp, n
elif metric == "fdr":
numerator, denominator = fp, (fp + tp)
elif metric == "for":
numerator, denominator = fn, (fn + tn)
elif metric == "pt":
tpr = handle_zero_divide(tp, p, div_0)
tnr = handle_zero_divide(tn, n, div_0)
numerator = np.sqrt(tpr * (1 - tnr)) + tnr - 1
denominator = tpr + tnr - 1
elif metric == "ts":
numerator, denominator = tp, (tp + fn + fp)
elif metric == "acc":
numerator, denominator = (tp + tp), (p + n)
elif metric == "ba":
tpr = handle_zero_divide(tp, p, div_0)
tnr = handle_zero_divide(tn, n, div_0)
numerator, denominator = (tpr + tnr), 2
elif metric == "f1":
numerator, denominator = tp * 2, (tp * 2 + fn + fp)
elif metric == "mcc":
numerator = tp * tn - fp * fn
denominator = np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
elif metric == "fm":
tpr = handle_zero_divide(tp, p, div_0)
ppv = handle_zero_divide(tp, (tp + fp), div_0)
numerator = np.sqrt(ppv * tpr)
denominator = 1
elif metric == "bm":
tpr = handle_zero_divide(tp, p, div_0)
tnr = handle_zero_divide(tn, n, div_0)
numerator = tpr + tnr - 1
denominator = 1
elif metric == "mk":
ppv = handle_zero_divide(tp, (tp + fp), div_0)
npv = handle_zero_divide(tn, (tn + fn), div_0)
numerator = ppv + npv - 1
denominator = 1
else:
raise NotImplementedError("the metric is not implemented.")
result = handle_zero_divide(numerator, denominator, div_0)
if average == Average.MICRO or average == Average.NONE:
return result
weights: Optional[np.ndarray]
if average == Average.MACRO:
weights = None
elif average == Average.WEIGHTED:
weights = p
result = np.average(result, weights=weights)
return result
def compute_roc_auc(
y_pred: torch.Tensor,
y: torch.Tensor,
to_onehot_y: bool = False,
softmax: bool = False,
other_act: Optional[Callable] = None,
average: Union[Average, str] = Average.MACRO,
):
"""Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC). Referring to:
`sklearn.metrics.roc_auc_score <https://scikit-learn.org/stable/modules/generated/
sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score>`_.
Args:
y_pred: input data to compute, typical classification model output.
it must be One-Hot format and first dim is batch, example shape: [16] or [16, 2].
y: ground truth to compute ROC AUC metric, the first dim is batch.
example shape: [16, 1] will be converted into [16, 2] (where `2` is inferred from `y_pred`).
to_onehot_y: whether to convert `y` into the one-hot format. Defaults to False.
softmax: whether to add softmax function to `y_pred` before computation. Defaults to False.
other_act: callable function to replace `softmax` as activation layer if needed, Defaults to ``None``.
for example: `other_act = lambda x: torch.log_softmax(x)`.
average: {``"macro"``, ``"weighted"``, ``"micro"``, ``"none"``}
Type of averaging performed if not binary classification.
Defaults to ``"macro"``.
- ``"macro"``: calculate metrics for each label, and find their unweighted mean.
This does not take label imbalance into account.
- ``"weighted"``: calculate metrics for each label, and find their average,
weighted by support (the number of true instances for each label).
- ``"micro"``: calculate metrics globally by considering each element of the label
indicator matrix as a label.
- ``"none"``: the scores for each class are returned.
Raises:
ValueError: When ``y_pred`` dimension is not one of [1, 2].
ValueError: When ``y`` dimension is not one of [1, 2].
ValueError: When ``softmax=True`` and ``other_act is not None``. Incompatible values.
TypeError: When ``other_act`` is not an ``Optional[Callable]``.
ValueError: When ``average`` is not one of ["macro", "weighted", "micro", "none"].
Note:
ROCAUC expects y to be comprised of 0's and 1's. `y_pred` must be either prob. estimates or confidence values.
"""
y_pred_ndim = y_pred.ndimension()
y_ndim = y.ndimension()
if y_pred_ndim not in (1, 2):
raise ValueError(
"Predictions should be of shape (batch_size, n_classes) or (batch_size, )."
)
if y_ndim not in (1, 2):
raise ValueError(
"Targets should be of shape (batch_size, n_classes) or (batch_size, )."
)
if y_pred_ndim == 2 and y_pred.shape[1] == 1:
y_pred = y_pred.squeeze(dim=-1)
y_pred_ndim = 1
if y_ndim == 2 and y.shape[1] == 1:
y = y.squeeze(dim=-1)
if y_pred_ndim == 1:
if to_onehot_y:
warnings.warn(
"y_pred has only one channel, to_onehot_y=True ignored.")
if softmax:
warnings.warn("y_pred has only one channel, softmax=True ignored.")
return _calculate(y, y_pred)
else:
n_classes = y_pred.shape[1]
if to_onehot_y:
y = one_hot(y, n_classes)
if softmax and other_act is not None:
raise ValueError(
"Incompatible values: softmax=True and other_act is not None.")
if softmax:
y_pred = y_pred.float().softmax(dim=1)
if other_act is not None:
if not callable(other_act):
raise TypeError(
f"other_act must be None or callable but is {type(other_act).__name__}."
)
y_pred = other_act(y_pred)
assert y.shape == y_pred.shape, "data shapes of y_pred and y do not match."
average = Average(average)
if average == Average.MICRO:
return _calculate(y.flatten(), y_pred.flatten())
else:
y, y_pred = y.transpose(0, 1), y_pred.transpose(0, 1)
auc_values = [
_calculate(y_, y_pred_) for y_, y_pred_ in zip(y, y_pred)
]
if average == Average.NONE:
return auc_values
if average == Average.MACRO:
return np.mean(auc_values)
if average == Average.WEIGHTED:
weights = [sum(y_) for y_ in y]
return np.average(auc_values, weights=weights)
raise ValueError(
f'Unsupported average: {average}, available options are ["macro", "weighted", "micro", "none"].'
)
def _compute_fn(pred, label):
return compute_roc_auc(
y_pred=pred,
y=label,
average=Average(average),
)
def compute_roc_auc(
y_pred: torch.Tensor,
y: torch.Tensor,
average: Union[Average, str] = Average.MACRO,
):
"""Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC). Referring to:
`sklearn.metrics.roc_auc_score <https://scikit-learn.org/stable/modules/generated/
sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score>`_.
Args:
y_pred: input data to compute, typical classification model output.
it must be One-Hot format and first dim is batch, example shape: [16] or [16, 2].
y: ground truth to compute ROC AUC metric, the first dim is batch.
example shape: [16, 1] will be converted into [16, 2] (where `2` is inferred from `y_pred`).
average: {``"macro"``, ``"weighted"``, ``"micro"``, ``"none"``}
Type of averaging performed if not binary classification.
Defaults to ``"macro"``.
- ``"macro"``: calculate metrics for each label, and find their unweighted mean.
This does not take label imbalance into account.
- ``"weighted"``: calculate metrics for each label, and find their average,
weighted by support (the number of true instances for each label).
- ``"micro"``: calculate metrics globally by considering each element of the label
indicator matrix as a label.
- ``"none"``: the scores for each class are returned.
Raises:
ValueError: When ``y_pred`` dimension is not one of [1, 2].
ValueError: When ``y`` dimension is not one of [1, 2].
ValueError: When ``average`` is not one of ["macro", "weighted", "micro", "none"].
Note:
ROCAUC expects y to be comprised of 0's and 1's. `y_pred` must be either prob. estimates or confidence values.
"""
y_pred_ndim = y_pred.ndimension()
y_ndim = y.ndimension()
if y_pred_ndim not in (1, 2):
raise ValueError(
"Predictions should be of shape (batch_size, n_classes) or (batch_size, )."
)
if y_ndim not in (1, 2):
raise ValueError(
"Targets should be of shape (batch_size, n_classes) or (batch_size, )."
)
if y_pred_ndim == 2 and y_pred.shape[1] == 1:
y_pred = y_pred.squeeze(dim=-1)
y_pred_ndim = 1
if y_ndim == 2 and y.shape[1] == 1:
y = y.squeeze(dim=-1)
if y_pred_ndim == 1:
return _calculate(y_pred, y)
if y.shape != y_pred.shape:
raise AssertionError("data shapes of y_pred and y do not match.")
average = Average(average)
if average == Average.MICRO:
return _calculate(y_pred.flatten(), y.flatten())
y, y_pred = y.transpose(0, 1), y_pred.transpose(0, 1)
auc_values = [_calculate(y_pred_, y_) for y_pred_, y_ in zip(y_pred, y)]
if average == Average.NONE:
return auc_values
if average == Average.MACRO:
return np.mean(auc_values)
if average == Average.WEIGHTED:
weights = [sum(y_) for y_ in y]
return np.average(auc_values, weights=weights)
raise ValueError(
f'Unsupported average: {average}, available options are ["macro", "weighted", "micro", "none"].'
)
