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__init__.py
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__init__.py
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# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from torchmetrics.functional.classification.accuracy import ( # noqa: F401
accuracy,
binary_accuracy,
multiclass_accuracy,
multilabel_accuracy,
)
from torchmetrics.functional.classification.auroc import ( # noqa: F401
auroc,
binary_auroc,
multiclass_auroc,
multilabel_auroc,
)
from torchmetrics.functional.classification.average_precision import ( # noqa: F401
average_precision,
binary_average_precision,
multiclass_average_precision,
multilabel_average_precision,
)
from torchmetrics.functional.classification.calibration_error import ( # noqa: F401
binary_calibration_error,
calibration_error,
multiclass_calibration_error,
)
from torchmetrics.functional.classification.cohen_kappa import ( # noqa: F401
binary_cohen_kappa,
cohen_kappa,
multiclass_cohen_kappa,
)
from torchmetrics.functional.classification.confusion_matrix import ( # noqa: F401
binary_confusion_matrix,
confusion_matrix,
multiclass_confusion_matrix,
multilabel_confusion_matrix,
)
from torchmetrics.functional.classification.dice import dice, dice_score # noqa: F401
from torchmetrics.functional.classification.exact_match import ( # noqa: F401
exact_match,
multiclass_exact_match,
multilabel_exact_match,
)
from torchmetrics.functional.classification.f_beta import ( # noqa: F401
binary_f1_score,
binary_fbeta_score,
f1_score,
fbeta_score,
multiclass_f1_score,
multiclass_fbeta_score,
multilabel_f1_score,
multilabel_fbeta_score,
)
from torchmetrics.functional.classification.hamming import ( # noqa: F401
binary_hamming_distance,
hamming_distance,
multiclass_hamming_distance,
multilabel_hamming_distance,
)
from torchmetrics.functional.classification.hinge import ( # noqa: F401
binary_hinge_loss,
hinge_loss,
multiclass_hinge_loss,
)
from torchmetrics.functional.classification.jaccard import ( # noqa: F401
binary_jaccard_index,
jaccard_index,
multiclass_jaccard_index,
multilabel_jaccard_index,
)
from torchmetrics.functional.classification.matthews_corrcoef import ( # noqa: F401
binary_matthews_corrcoef,
matthews_corrcoef,
multiclass_matthews_corrcoef,
multilabel_matthews_corrcoef,
)
from torchmetrics.functional.classification.precision_recall import ( # noqa: F401
binary_precision,
binary_recall,
multiclass_precision,
multiclass_recall,
multilabel_precision,
multilabel_recall,
precision,
precision_recall,
recall,
)
from torchmetrics.functional.classification.precision_recall_curve import ( # noqa: F401
binary_precision_recall_curve,
multiclass_precision_recall_curve,
multilabel_precision_recall_curve,
precision_recall_curve,
)
from torchmetrics.functional.classification.ranking import ( # noqa: F401
multilabel_coverage_error,
multilabel_ranking_average_precision,
multilabel_ranking_loss,
)
from torchmetrics.functional.classification.recall_at_fixed_precision import ( # noqa: F401
binary_recall_at_fixed_precision,
multiclass_recall_at_fixed_precision,
multilabel_recall_at_fixed_precision,
)
from torchmetrics.functional.classification.roc import binary_roc, multiclass_roc, multilabel_roc, roc # noqa: F401
from torchmetrics.functional.classification.specificity import ( # noqa: F401
binary_specificity,
multiclass_specificity,
multilabel_specificity,
specificity,
)
from torchmetrics.functional.classification.stat_scores import ( # noqa: F401
binary_stat_scores,
multiclass_stat_scores,
multilabel_stat_scores,
stat_scores,
)