Discrepancy between torch AUPRC and sklearn area under precision_recall_curve

I am writing a script that extends torch metrics to gives me some additional ease in using AUPRC and and AUROC in torch. Essentially I want them to be wrapped with a task parameter that allows me to select the task on init and an average parameter that allows me to select the micro average as in sklearns implementation (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html).

So while I did achieve that the problem is the following:

  • The values computed for auc pr are different in torchmetrics and sklearn

I can’t really explain why that is, since torch does not take thresholds as a paramter I can’t really increase them to a value where they wouldn’t matter. You can find the script here:

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<code>from torch import Tensor
from typing import Type, Literal, Optional, Union, List, Any
from torcheval.metrics import BinaryAUPRC, MulticlassAUPRC, MultilabelAUPRC, metric
from torchmetrics import AUROC as _AUROC
from copy import deepcopy
# TODO! This absolutetly needs testing
class AUPRC(metric.Metric[Tensor]):
def __new__(cls, task: str, num_labels: int = 1, average="macro"):
if average not in ["macro", "micro", "none"]:
raise ValueError("Average must be one of 'macro', 'micro', or 'none'"
f" but is {average}")
if task == "binary" or average == "micro":
metric = BinaryAUPRC()
elif task == "multiclass":
# Some debate in the net but in torch this is one-vs-all
metric = MulticlassAUPRC(num_classes=num_labels, average=average)
elif task == "multilabel":
# This is multiple positives allowed
metric = MultilabelAUPRC(num_labels=num_labels, average=average)
else:
raise ValueError("Unsupported task type or activation function")
metric._task = task
metric._average = average
return metric
def update(self, predictions, labels):
# Reshape predictions and labels to handle the batch dimension
if self._task == "binary" or self._average == "micro":
predictions = predictions.view(-1)
labels = labels.view(-1)
elif self._task == "multiclass":
labels = labels.view(-1)
self.metric.update(predictions, labels)
def to(self, device):
# Move the metric to the specified device
self.metric = self.metric.to(device)
return self
class AUROC(_AUROC):
def __new__(
cls: Type["_AUROC"],
task: Literal["binary", "multiclass", "multilabel"],
thresholds: Optional[Union[int, List[float], Tensor]] = None,
num_classes: Optional[int] = None,
average: Optional[Literal["macro", "weighted", "none", "micro"]] = "macro",
max_fpr: Optional[float] = None,
ignore_index: Optional[int] = None,
validate_args: bool = True,
**kwargs: Any,
):
if average == "micro" and task == "multilabel":
task = "binary"
metric = super().__new__(cls,
task=task,
thresholds=thresholds,
num_classes=num_classes,
num_labels=num_classes,
average="none" if average == "micro" else average,
max_fpr=max_fpr,
ignore_index=ignore_index,
validate_args=validate_args,
**kwargs)
metric._average = average
return metric
# You might want to override update and compute methods if needed
def update(self, input: Tensor, target: Tensor, weight: Tensor = None, *args, **kwargs) -> None:
if self._average == "micro":
target = target.view(-1)
input = input.view(-1)
return self.update(input, target, weight, *args, **kwargs)
def compute(self) -> Tensor:
return self.compute()
if __name__ == "__main__":
# Multi-class classification data
import torch
# Compute precision-recall curve
import numpy as np
from sklearn.metrics import roc_auc_score, precision_recall_curve, auc
#
y_true_multi = torch.Tensor([[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 0], [0, 1, 0], [0, 0, 1],
[1, 0, 0], [0, 0, 1], [1, 0, 0], [1, 0, 1]]).int()
y_pred_multi = torch.Tensor([[0.8, 0.1, 0.1], [0.2, 0.7, 0.1], [0.1, 0.3, 0.6], [0.7, 0.2, 0.1],
[0.1, 0.6, 0.3], [0.2, 0.1, 0.7], [0.6, 0.3, 0.1], [0.3, 0.5, 0.2],
[0.2, 0.1, 0.7], [0.7, 0.2, 0.1]])
# ---------------- Comparing Micro-Macro PR AUC using torch with sklearn --------------------
print("--- Comparing Micro-Macro ROC AUC ---")
# Compute ours: micro
micro_rocauc = AUROC(task="multilabel", average="micro", num_classes=3)
micro_rocauc.update(y_pred_multi, y_true_multi)
print("Micro AUCROC (torch):", micro_rocauc.compute())
# Compute ours: macro
macro_rocauc = AUROC(task="multilabel", average="macro", num_classes=3)
macro_rocauc.update(y_pred_multi, y_true_multi)
print("Micro AUCPRC (torch):", macro_rocauc.compute())
# Compute theirs
# Flatten y_true_multi as numpy
y_true_multi_flat = y_true_multi.numpy().flatten()
y_pred_multi_flat = y_pred_multi.numpy().flatten()
# Compute micro-average ROC AUC using sklearn
micro_rocauc_sklearn = roc_auc_score(y_true_multi,
y_pred_multi,
average='micro',
multi_class='ovr')
print(f'Micro-average auc-roc (sklearn): {micro_rocauc_sklearn:.4f}')
# Compute macro-average ROC AUC using sklearn
macro_rocauc_sklearn = roc_auc_score(y_true_multi,
y_pred_multi,
average='macro',
multi_class='ovr')
print(f'Macro-average auc-roc (sklearn): {macro_rocauc_sklearn:.4f}')
# ---------------- Comparing Micro-Macro PR AUC using torch with sklearn --------------------
print("--- Comparing Micro-Macro PR AUC ---")
micro_prauc = AUPRC(task="multilabel", num_labels=3, average="micro")
macro_prauc = AUPRC(task="multilabel", num_labels=3, average="macro")
# Compute ours
for idx in range(len(y_true_multi)):
yt = y_true_multi[idx, :].unsqueeze(0)
yp = y_pred_multi[idx, :].unsqueeze(0)
micro_prauc.update(yp, yt)
macro_prauc.update(yp, yt)
print("Micro AUCPR (torch):", micro_prauc.compute())
print("Macro AUCPR (torch):", macro_prauc.compute())
# Compute theirs
roc_pr_list = []
roc_auc_list = []
# Iterate over each class
for i in range(y_true_multi.shape[1]):
y_true = y_true_multi[:, i]
y_pred = y_pred_multi[:, i]
# Compute precision-recall curve
precision, recall, _ = precision_recall_curve(y_true, y_pred)
roc_pr_list.append(auc(recall, precision))
# Compute ROC AUC score
roc_auc = roc_auc_score(y_true, y_pred)
roc_auc_list.append(roc_auc)
print(f"PR AUC macro Score (sklearn): {np.mean(roc_pr_list)}")
precision, recall, _ = precision_recall_curve(y_true_multi_flat, y_pred_multi_flat)
pr_auc = auc(recall, precision)
# Print results
print(f"PR AUC micro Score (sklearn): {pr_auc}")
print()
# ---------------- Comparing Binary PR AUC using torch with sklearn --------------------
prauc = AUPRC(task="binary")
prauc.update(y_pred_multi.flatten(), y_true_multi.flatten())
print("Binary AUCPR (torch):", prauc.compute())
from torcheval.metrics.functional import binary_auprc
binary_auprc(y_pred_multi.flatten(), y_true_multi.flatten())
print("Binary AUCPR functional (torch):", prauc.compute())
precision, recall, _ = precision_recall_curve(y_true_multi_flat, y_pred_multi_flat)
pr_auc = auc(recall, precision)
# Print results
print(f"Binary PRAUC Score (sklearn): {pr_auc}")
</code>
<code>from torch import Tensor from typing import Type, Literal, Optional, Union, List, Any from torcheval.metrics import BinaryAUPRC, MulticlassAUPRC, MultilabelAUPRC, metric from torchmetrics import AUROC as _AUROC from copy import deepcopy # TODO! This absolutetly needs testing class AUPRC(metric.Metric[Tensor]): def __new__(cls, task: str, num_labels: int = 1, average="macro"): if average not in ["macro", "micro", "none"]: raise ValueError("Average must be one of 'macro', 'micro', or 'none'" f" but is {average}") if task == "binary" or average == "micro": metric = BinaryAUPRC() elif task == "multiclass": # Some debate in the net but in torch this is one-vs-all metric = MulticlassAUPRC(num_classes=num_labels, average=average) elif task == "multilabel": # This is multiple positives allowed metric = MultilabelAUPRC(num_labels=num_labels, average=average) else: raise ValueError("Unsupported task type or activation function") metric._task = task metric._average = average return metric def update(self, predictions, labels): # Reshape predictions and labels to handle the batch dimension if self._task == "binary" or self._average == "micro": predictions = predictions.view(-1) labels = labels.view(-1) elif self._task == "multiclass": labels = labels.view(-1) self.metric.update(predictions, labels) def to(self, device): # Move the metric to the specified device self.metric = self.metric.to(device) return self class AUROC(_AUROC): def __new__( cls: Type["_AUROC"], task: Literal["binary", "multiclass", "multilabel"], thresholds: Optional[Union[int, List[float], Tensor]] = None, num_classes: Optional[int] = None, average: Optional[Literal["macro", "weighted", "none", "micro"]] = "macro", max_fpr: Optional[float] = None, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any, ): if average == "micro" and task == "multilabel": task = "binary" metric = super().__new__(cls, task=task, thresholds=thresholds, num_classes=num_classes, num_labels=num_classes, average="none" if average == "micro" else average, max_fpr=max_fpr, ignore_index=ignore_index, validate_args=validate_args, **kwargs) metric._average = average return metric # You might want to override update and compute methods if needed def update(self, input: Tensor, target: Tensor, weight: Tensor = None, *args, **kwargs) -> None: if self._average == "micro": target = target.view(-1) input = input.view(-1) return self.update(input, target, weight, *args, **kwargs) def compute(self) -> Tensor: return self.compute() if __name__ == "__main__": # Multi-class classification data import torch # Compute precision-recall curve import numpy as np from sklearn.metrics import roc_auc_score, precision_recall_curve, auc # y_true_multi = torch.Tensor([[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 0], [0, 0, 1], [1, 0, 0], [1, 0, 1]]).int() y_pred_multi = torch.Tensor([[0.8, 0.1, 0.1], [0.2, 0.7, 0.1], [0.1, 0.3, 0.6], [0.7, 0.2, 0.1], [0.1, 0.6, 0.3], [0.2, 0.1, 0.7], [0.6, 0.3, 0.1], [0.3, 0.5, 0.2], [0.2, 0.1, 0.7], [0.7, 0.2, 0.1]]) # ---------------- Comparing Micro-Macro PR AUC using torch with sklearn -------------------- print("--- Comparing Micro-Macro ROC AUC ---") # Compute ours: micro micro_rocauc = AUROC(task="multilabel", average="micro", num_classes=3) micro_rocauc.update(y_pred_multi, y_true_multi) print("Micro AUCROC (torch):", micro_rocauc.compute()) # Compute ours: macro macro_rocauc = AUROC(task="multilabel", average="macro", num_classes=3) macro_rocauc.update(y_pred_multi, y_true_multi) print("Micro AUCPRC (torch):", macro_rocauc.compute()) # Compute theirs # Flatten y_true_multi as numpy y_true_multi_flat = y_true_multi.numpy().flatten() y_pred_multi_flat = y_pred_multi.numpy().flatten() # Compute micro-average ROC AUC using sklearn micro_rocauc_sklearn = roc_auc_score(y_true_multi, y_pred_multi, average='micro', multi_class='ovr') print(f'Micro-average auc-roc (sklearn): {micro_rocauc_sklearn:.4f}') # Compute macro-average ROC AUC using sklearn macro_rocauc_sklearn = roc_auc_score(y_true_multi, y_pred_multi, average='macro', multi_class='ovr') print(f'Macro-average auc-roc (sklearn): {macro_rocauc_sklearn:.4f}') # ---------------- Comparing Micro-Macro PR AUC using torch with sklearn -------------------- print("--- Comparing Micro-Macro PR AUC ---") micro_prauc = AUPRC(task="multilabel", num_labels=3, average="micro") macro_prauc = AUPRC(task="multilabel", num_labels=3, average="macro") # Compute ours for idx in range(len(y_true_multi)): yt = y_true_multi[idx, :].unsqueeze(0) yp = y_pred_multi[idx, :].unsqueeze(0) micro_prauc.update(yp, yt) macro_prauc.update(yp, yt) print("Micro AUCPR (torch):", micro_prauc.compute()) print("Macro AUCPR (torch):", macro_prauc.compute()) # Compute theirs roc_pr_list = [] roc_auc_list = [] # Iterate over each class for i in range(y_true_multi.shape[1]): y_true = y_true_multi[:, i] y_pred = y_pred_multi[:, i] # Compute precision-recall curve precision, recall, _ = precision_recall_curve(y_true, y_pred) roc_pr_list.append(auc(recall, precision)) # Compute ROC AUC score roc_auc = roc_auc_score(y_true, y_pred) roc_auc_list.append(roc_auc) print(f"PR AUC macro Score (sklearn): {np.mean(roc_pr_list)}") precision, recall, _ = precision_recall_curve(y_true_multi_flat, y_pred_multi_flat) pr_auc = auc(recall, precision) # Print results print(f"PR AUC micro Score (sklearn): {pr_auc}") print() # ---------------- Comparing Binary PR AUC using torch with sklearn -------------------- prauc = AUPRC(task="binary") prauc.update(y_pred_multi.flatten(), y_true_multi.flatten()) print("Binary AUCPR (torch):", prauc.compute()) from torcheval.metrics.functional import binary_auprc binary_auprc(y_pred_multi.flatten(), y_true_multi.flatten()) print("Binary AUCPR functional (torch):", prauc.compute()) precision, recall, _ = precision_recall_curve(y_true_multi_flat, y_pred_multi_flat) pr_auc = auc(recall, precision) # Print results print(f"Binary PRAUC Score (sklearn): {pr_auc}") </code>
from torch import Tensor
from typing import Type, Literal, Optional, Union, List, Any
from torcheval.metrics import BinaryAUPRC, MulticlassAUPRC, MultilabelAUPRC, metric
from torchmetrics import AUROC as _AUROC
from copy import deepcopy

# TODO! This absolutetly needs testing


class AUPRC(metric.Metric[Tensor]):

    def __new__(cls, task: str, num_labels: int = 1, average="macro"):
        if average not in ["macro", "micro", "none"]:
            raise ValueError("Average must be one of 'macro', 'micro', or 'none'"
                             f" but is {average}")

        if task == "binary" or average == "micro":
            metric = BinaryAUPRC()
        elif task == "multiclass":
            # Some debate in the net but in torch this is one-vs-all
            metric = MulticlassAUPRC(num_classes=num_labels, average=average)
        elif task == "multilabel":
            # This is multiple positives allowed
            metric = MultilabelAUPRC(num_labels=num_labels, average=average)
        else:
            raise ValueError("Unsupported task type or activation function")
        metric._task = task
        metric._average = average

        return metric

    def update(self, predictions, labels):
        # Reshape predictions and labels to handle the batch dimension
        if self._task == "binary" or self._average == "micro":
            predictions = predictions.view(-1)
            labels = labels.view(-1)
        elif self._task == "multiclass":
            labels = labels.view(-1)

        self.metric.update(predictions, labels)

    def to(self, device):
        # Move the metric to the specified device
        self.metric = self.metric.to(device)
        return self


class AUROC(_AUROC):

    def __new__(
        cls: Type["_AUROC"],
        task: Literal["binary", "multiclass", "multilabel"],
        thresholds: Optional[Union[int, List[float], Tensor]] = None,
        num_classes: Optional[int] = None,
        average: Optional[Literal["macro", "weighted", "none", "micro"]] = "macro",
        max_fpr: Optional[float] = None,
        ignore_index: Optional[int] = None,
        validate_args: bool = True,
        **kwargs: Any,
    ):
        if average == "micro" and task == "multilabel":
            task = "binary"

        metric = super().__new__(cls,
                                 task=task,
                                 thresholds=thresholds,
                                 num_classes=num_classes,
                                 num_labels=num_classes,
                                 average="none" if average == "micro" else average,
                                 max_fpr=max_fpr,
                                 ignore_index=ignore_index,
                                 validate_args=validate_args,
                                 **kwargs)
        metric._average = average
        return metric

    # You might want to override update and compute methods if needed
    def update(self, input: Tensor, target: Tensor, weight: Tensor = None, *args, **kwargs) -> None:
        if self._average == "micro":
            target = target.view(-1)
            input = input.view(-1)
        return self.update(input, target, weight, *args, **kwargs)

    def compute(self) -> Tensor:
        return self.compute()


if __name__ == "__main__":
    # Multi-class classification data
    import torch
    # Compute precision-recall curve
    import numpy as np
    from sklearn.metrics import roc_auc_score, precision_recall_curve, auc

    #
    y_true_multi = torch.Tensor([[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 0], [0, 1, 0], [0, 0, 1],
                                 [1, 0, 0], [0, 0, 1], [1, 0, 0], [1, 0, 1]]).int()
    y_pred_multi = torch.Tensor([[0.8, 0.1, 0.1], [0.2, 0.7, 0.1], [0.1, 0.3, 0.6], [0.7, 0.2, 0.1],
                                 [0.1, 0.6, 0.3], [0.2, 0.1, 0.7], [0.6, 0.3, 0.1], [0.3, 0.5, 0.2],
                                 [0.2, 0.1, 0.7], [0.7, 0.2, 0.1]])

    # ---------------- Comparing Micro-Macro PR AUC using torch with sklearn --------------------
    print("--- Comparing Micro-Macro ROC AUC ---")

    # Compute ours: micro
    micro_rocauc = AUROC(task="multilabel", average="micro", num_classes=3)
    micro_rocauc.update(y_pred_multi, y_true_multi)
    print("Micro AUCROC (torch):", micro_rocauc.compute())

    # Compute ours: macro
    macro_rocauc = AUROC(task="multilabel", average="macro", num_classes=3)
    macro_rocauc.update(y_pred_multi, y_true_multi)
    print("Micro AUCPRC (torch):", macro_rocauc.compute())

    # Compute theirs
    # Flatten y_true_multi as numpy
    y_true_multi_flat = y_true_multi.numpy().flatten()
    y_pred_multi_flat = y_pred_multi.numpy().flatten()

    # Compute micro-average ROC AUC using sklearn
    micro_rocauc_sklearn = roc_auc_score(y_true_multi,
                                         y_pred_multi,
                                         average='micro',
                                         multi_class='ovr')
    print(f'Micro-average auc-roc (sklearn): {micro_rocauc_sklearn:.4f}')

    # Compute macro-average ROC AUC using sklearn
    macro_rocauc_sklearn = roc_auc_score(y_true_multi,
                                         y_pred_multi,
                                         average='macro',
                                         multi_class='ovr')
    print(f'Macro-average auc-roc (sklearn): {macro_rocauc_sklearn:.4f}')

    # ---------------- Comparing Micro-Macro PR AUC using torch with sklearn --------------------
    print("--- Comparing Micro-Macro PR AUC ---")
    micro_prauc = AUPRC(task="multilabel", num_labels=3, average="micro")
    macro_prauc = AUPRC(task="multilabel", num_labels=3, average="macro")

    # Compute ours
    for idx in range(len(y_true_multi)):
        yt = y_true_multi[idx, :].unsqueeze(0)
        yp = y_pred_multi[idx, :].unsqueeze(0)
        micro_prauc.update(yp, yt)
        macro_prauc.update(yp, yt)

    print("Micro AUCPR (torch):", micro_prauc.compute())
    print("Macro AUCPR (torch):", macro_prauc.compute())

    # Compute theirs
    roc_pr_list = []
    roc_auc_list = []

    # Iterate over each class
    for i in range(y_true_multi.shape[1]):
        y_true = y_true_multi[:, i]
        y_pred = y_pred_multi[:, i]

        # Compute precision-recall curve
        precision, recall, _ = precision_recall_curve(y_true, y_pred)
        roc_pr_list.append(auc(recall, precision))

        # Compute ROC AUC score
        roc_auc = roc_auc_score(y_true, y_pred)
        roc_auc_list.append(roc_auc)

    print(f"PR AUC macro Score (sklearn): {np.mean(roc_pr_list)}")

    precision, recall, _ = precision_recall_curve(y_true_multi_flat, y_pred_multi_flat)
    pr_auc = auc(recall, precision)

    # Print results
    print(f"PR AUC micro Score (sklearn): {pr_auc}")
    print()
    # ---------------- Comparing Binary PR AUC using torch with sklearn --------------------
    prauc = AUPRC(task="binary")
    prauc.update(y_pred_multi.flatten(), y_true_multi.flatten())

    print("Binary AUCPR (torch):", prauc.compute())
    from torcheval.metrics.functional import binary_auprc
    binary_auprc(y_pred_multi.flatten(), y_true_multi.flatten())
    print("Binary AUCPR functional (torch):", prauc.compute())
    precision, recall, _ = precision_recall_curve(y_true_multi_flat, y_pred_multi_flat)
    pr_auc = auc(recall, precision)

    # Print results
    print(f"Binary PRAUC Score (sklearn): {pr_auc}")

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Trang chủ Giới thiệu Sinh nhật bé trai Sinh nhật bé gái Tổ chức sự kiện Biểu diễn giải trí Dịch vụ khác Trang trí tiệc cưới Tổ chức khai trương Tư vấn dịch vụ Thư viện ảnh Tin tức - sự kiện Liên hệ Chú hề sinh nhật Trang trí YEAR END PARTY công ty Trang trí tất niên cuối năm Trang trí tất niên xu hướng mới nhất Trang trí sinh nhật bé trai Hải Đăng Trang trí sinh nhật bé Khánh Vân Trang trí sinh nhật Bích Ngân Trang trí sinh nhật bé Thanh Trang Thuê ông già Noel phát quà Biểu diễn xiếc khỉ Xiếc quay đĩa
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