from tests.classification.inputs import _input_multiclass_prob as _mc_prob
from tests.classification.inputs import _input_multidim_multiclass as _mdmc
from tests.classification.inputs import _input_multidim_multiclass_prob as _mdmc_prob
from tests.classification.inputs import _input_multilabel as _ml
from tests.classification.inputs import _input_multilabel_multidim as _mlmd
from tests.classification.inputs import _input_multilabel_multidim_prob as _mlmd_prob
from tests.classification.inputs import _input_multilabel_prob as _ml_prob
from tests.helpers.testers import BATCH_SIZE, EXTRA_DIM, NUM_BATCHES, NUM_CLASSES, THRESHOLD
from torchmetrics.utilities.checks import _input_format_classification
from torchmetrics.utilities.data import select_topk, to_onehot
from torchmetrics.utilities.enums import DataType
torch.manual_seed(42)
# Some additional inputs to test on
_ml_prob_half = Input(_ml_prob.preds.half(), _ml_prob.target)
_mc_prob_2cls_preds = rand(NUM_BATCHES, BATCH_SIZE, 2)
_mc_prob_2cls_preds /= _mc_prob_2cls_preds.sum(dim=2, keepdim=True)
_mc_prob_2cls = Input(_mc_prob_2cls_preds,
randint(high=2, size=(NUM_BATCHES, BATCH_SIZE)))
_mdmc_prob_many_dims_preds = rand(NUM_BATCHES, BATCH_SIZE, NUM_CLASSES,
EXTRA_DIM, EXTRA_DIM)
_mdmc_prob_many_dims_preds /= _mdmc_prob_many_dims_preds.sum(dim=2,
keepdim=True)
_mdmc_prob_many_dims = Input(
_mdmc_prob_many_dims_preds,
randint(high=2, size=(NUM_BATCHES, BATCH_SIZE, EXTRA_DIM, EXTRA_DIM)),
)
import numpy as np
import pytest
import torch
from sklearn.metrics import hinge_loss as sk_hinge
from sklearn.preprocessing import OneHotEncoder
from tests.classification.inputs import Input
from tests.helpers.testers import BATCH_SIZE, NUM_BATCHES, NUM_CLASSES, MetricTester
from torchmetrics import Hinge
from torchmetrics.functional import hinge
from torchmetrics.functional.classification.hinge import MulticlassMode
torch.manual_seed(42)
_input_binary = Input(preds=torch.randn(NUM_BATCHES, BATCH_SIZE),
target=torch.randint(high=2,
size=(NUM_BATCHES, BATCH_SIZE)))
_input_binary_single = Input(preds=torch.randn((NUM_BATCHES, 1)),
target=torch.randint(high=2,
size=(NUM_BATCHES, 1)))
_input_multiclass = Input(preds=torch.randn(NUM_BATCHES, BATCH_SIZE,
NUM_CLASSES),
target=torch.randint(high=NUM_CLASSES,
size=(NUM_BATCHES, BATCH_SIZE)))
def _sk_hinge(preds, target, squared, multiclass_mode):
sk_preds, sk_target = preds.numpy(), target.numpy()