Add LogCosh Error

CHANGELOG.md

@@ -19,6 +19,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added `KendallRankCorrCoef` to regression package ([#1271](https://github.com/Lightning-AI/metrics/pull/1271))
 
 
+- Added `LogCoshError` to regression package ([#1316](https://github.com/Lightning-AI/metrics/pull/1316))
+
+
 ### Changed
 
 - Changed `MeanAveragePrecision` to vectorize `_find_best_gt_match` operation ([#1259](https://github.com/Lightning-AI/metrics/pull/1259))

docs/source/links.rst

@@ -95,3 +95,4 @@
 .. _Fisher-Rao distance: http://www.scholarpedia.org/article/Fisher-Rao_metric
 .. _Kendall Rank Correlation Coefficient: https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient
 .. _The Treatment of Ties in Ranking Problems: https://www.jstor.org/stable/2332303
+.. _LogCosh Error: https://arxiv.org/pdf/2101.10427.pdf

docs/source/regression/log_cosh_error.rst

@@ -0,0 +1,22 @@
+.. customcarditem::
+   :header: Log Cosh Error
+   :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/tabular_classification.svg
+   :tags: Regression
+
+.. include:: ../links.rst
+
+##############
+Log Cosh Error
+##############
+
+Module Interface
+________________
+
+.. autoclass:: torchmetrics.LogCoshError
+    :noindex:
+
+Functional Interface
+____________________
+
+.. autofunction:: torchmetrics.functional.log_cosh_error
+    :noindex:

src/torchmetrics/__init__.py

@@ -59,6 +59,7 @@
     ExplainedVariance,
     KendallRankCorrCoef,
     KLDivergence,
+    LogCoshError,
     MeanAbsoluteError,
     MeanAbsolutePercentageError,
     MeanSquaredError,
@@ -132,6 +133,7 @@
     "JaccardIndex",
     "KendallRankCorrCoef",
     "KLDivergence",
+    "LogCoshError",
     "MatchErrorRate",
     "MatthewsCorrCoef",
     "MaxMetric",

src/torchmetrics/functional/__init__.py

@@ -51,6 +51,7 @@
 from torchmetrics.functional.regression.explained_variance import explained_variance
 from torchmetrics.functional.regression.kendall import kendall_rank_corrcoef
 from torchmetrics.functional.regression.kl_divergence import kl_divergence
+from torchmetrics.functional.regression.log_cosh import log_cosh_error
 from torchmetrics.functional.regression.log_mse import mean_squared_log_error
 from torchmetrics.functional.regression.mae import mean_absolute_error
 from torchmetrics.functional.regression.mape import mean_absolute_percentage_error
@@ -115,6 +116,7 @@
     "jaccard_index",
     "kendall_rank_corrcoef",
     "kl_divergence",
+    "log_cosh_error",
     "match_error_rate",
     "matthews_corrcoef",
     "mean_absolute_error",

src/torchmetrics/functional/regression/__init__.py

@@ -16,6 +16,7 @@
 from torchmetrics.functional.regression.explained_variance import explained_variance  # noqa: F401
 from torchmetrics.functional.regression.kendall import kendall_rank_corrcoef  # noqa: F401
 from torchmetrics.functional.regression.kl_divergence import kl_divergence  # noqa: F401
+from torchmetrics.functional.regression.log_cosh import log_cosh_error  # noqa: F401
 from torchmetrics.functional.regression.log_mse import mean_squared_log_error  # noqa: F401
 from torchmetrics.functional.regression.mae import mean_absolute_error  # noqa: F401
 from torchmetrics.functional.regression.mape import mean_absolute_percentage_error  # noqa: F401

src/torchmetrics/functional/regression/kendall.py

@@ -17,7 +17,7 @@
 from torch import Tensor
 from typing_extensions import Literal
 
-from torchmetrics.functional.regression.utils import _check_data_shape_for_corr_coef
+from torchmetrics.functional.regression.utils import _check_data_shape_to_num_outputs
 from torchmetrics.utilities.checks import _check_same_shape
 from torchmetrics.utilities.data import _bincount, dim_zero_cat
 from torchmetrics.utilities.enums import EnumStr
@@ -265,7 +265,7 @@ def _kendall_corrcoef_update(
     """
     # Data checking
     _check_same_shape(preds, target)
-    _check_data_shape_for_corr_coef(preds, target, num_outputs)
+    _check_data_shape_to_num_outputs(preds, target, num_outputs)
 
     if num_outputs == 1:
         preds = preds.unsqueeze(1)

src/torchmetrics/functional/regression/log_cosh.py

@@ -0,0 +1,92 @@
+# 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 typing import Tuple
+
+import torch
+from torch import Tensor
+
+from torchmetrics.functional.regression.utils import _check_data_shape_to_num_outputs
+from torchmetrics.utilities.checks import _check_same_shape
+
+
+def _unsqueeze_tensors(preds: Tensor, target: Tensor) -> Tuple[Tensor, Tensor]:
+    if preds.ndim == 2:
+        return preds, target
+    return preds.unsqueeze(1), target.unsqueeze(1)
+
+
+def _log_cosh_error_update(preds: Tensor, target: Tensor, num_outputs: int) -> Tuple[Tensor, Tensor]:
+    """Updates and returns variables required to compute LogCosh error.
+
+    Checks for same shape of input tensors.
+
+    Args:
+        preds: Predicted tensor
+        target: Ground truth tensor
+
+    Return:
+        Sum of LogCosh error over examples, and total number of examples
+    """
+    _check_same_shape(preds, target)
+    _check_data_shape_to_num_outputs(preds, target, num_outputs)
+
+    preds, target = _unsqueeze_tensors(preds, target)
+    diff = preds - target
+    sum_log_cosh_error = torch.log((torch.exp(diff) + torch.exp(-diff)) / 2).sum(0).squeeze()
+    n_obs = torch.tensor(target.shape[0], device=preds.device)
+    return sum_log_cosh_error, n_obs
+
+
+def _log_cosh_error_compute(sum_log_cosh_error: Tensor, n_obs: Tensor) -> Tensor:
+    """Computes Mean Squared Error.
+
+    Args:
+        sum_squared_error: Sum of LogCosh errors over all observations
+        n_obs: Number of predictions or observations
+    """
+    return (sum_log_cosh_error / n_obs).squeeze()
+
+
+def log_cosh_error(preds: Tensor, target: Tensor) -> Tensor:
+    r"""Compute the `LogCosh Error`_.
+
+    .. math:: \text{LogCoshError} = \log\left(\frac{\exp(\hat{y} - y) + \exp(\hat{y - y})}{2}\right)
+
+    Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.
+
+    Args:
+        preds: estimated labels with shape ``(batch_size,)`` or `(batch_size, num_outputs)``
+        target: ground truth labels with shape ``(batch_size,)`` or `(batch_size, num_outputs)``
+
+    Return:
+        Tensor with LogCosh error
+
+    Example (single output regression)::
+        >>> from torchmetrics.functional import log_cosh_error
+        >>> preds = torch.tensor([3.0, 5.0, 2.5, 7.0])
+        >>> target = torch.tensor([2.5, 5.0, 4.0, 8.0])
+        >>> log_cosh_error(preds, target)
+        tensor(0.3523)
+
+    Example (multi output regression)::
+        >>> from torchmetrics.functional import log_cosh_error
+        >>> preds = torch.tensor([[3.0, 5.0, 1.2], [-2.1, 2.5, 7.0]])
+        >>> target = torch.tensor([[2.5, 5.0, 1.3], [0.3, 4.0, 8.0]])
+        >>> log_cosh_error(preds, target)
+        tensor([0.9176, 0.4277, 0.2194])
+    """
+    sum_log_cosh_error, n_obs = _log_cosh_error_update(
+        preds, target, num_outputs=1 if preds.ndim == 1 else preds.shape[-1]
+    )
+    return _log_cosh_error_compute(sum_log_cosh_error, n_obs)

src/torchmetrics/functional/regression/pearson.py

@@ -16,7 +16,7 @@
 import torch
 from torch import Tensor
 
-from torchmetrics.functional.regression.utils import _check_data_shape_for_corr_coef
+from torchmetrics.functional.regression.utils import _check_data_shape_to_num_outputs
 from torchmetrics.utilities.checks import _check_same_shape
 
 
@@ -45,7 +45,7 @@ def _pearson_corrcoef_update(
     """
     # Data checking
     _check_same_shape(preds, target)
-    _check_data_shape_for_corr_coef(preds, target, num_outputs)
+    _check_data_shape_to_num_outputs(preds, target, num_outputs)
 
     n_obs = preds.shape[0]
     mx_new = (n_prior * mean_x + preds.mean(0) * n_obs) / (n_prior + n_obs)

src/torchmetrics/functional/regression/spearman.py

@@ -16,7 +16,7 @@
 import torch
 from torch import Tensor
 
-from torchmetrics.functional.regression.utils import _check_data_shape_for_corr_coef
+from torchmetrics.functional.regression.utils import _check_data_shape_to_num_outputs
 from torchmetrics.utilities.checks import _check_same_shape
 
 
@@ -68,7 +68,7 @@ def _spearman_corrcoef_update(preds: Tensor, target: Tensor, num_outputs: int) -
             "Expected `preds` and `target` both to be floating point tensors, but got {pred.dtype} and {target.dtype}"
         )
     _check_same_shape(preds, target)
-    _check_data_shape_for_corr_coef(preds, target, num_outputs)
+    _check_data_shape_to_num_outputs(preds, target, num_outputs)
 
     return preds, target
 

src/torchmetrics/functional/regression/utils.py

@@ -14,7 +14,7 @@
 from torch import Tensor
 
 
-def _check_data_shape_for_corr_coef(preds: Tensor, target: Tensor, num_outputs: int) -> None:
+def _check_data_shape_to_num_outputs(preds: Tensor, target: Tensor, num_outputs: int) -> None:
     """Check that predictions and target have the correct shape, else raise error."""
     if preds.ndim > 2 or target.ndim > 2:
         raise ValueError(

src/torchmetrics/regression/__init__.py

@@ -16,6 +16,7 @@
 from torchmetrics.regression.explained_variance import ExplainedVariance  # noqa: F401
 from torchmetrics.regression.kendall import KendallRankCorrCoef  # noqa: F401
 from torchmetrics.regression.kl_divergence import KLDivergence  # noqa: F401
+from torchmetrics.regression.log_cosh import LogCoshError  # noqa: F401
 from torchmetrics.regression.log_mse import MeanSquaredLogError  # noqa: F401
 from torchmetrics.regression.mae import MeanAbsoluteError  # noqa: F401
 from torchmetrics.regression.mape import MeanAbsolutePercentageError  # noqa: F401

src/torchmetrics/regression/log_cosh.py

@@ -0,0 +1,83 @@
+# 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 typing import Any
+
+import torch
+from torch import Tensor
+
+from torchmetrics.functional.regression.log_cosh import _log_cosh_error_compute, _log_cosh_error_update
+from torchmetrics.metric import Metric
+
+
+class LogCoshError(Metric):
+    r"""Compute the `LogCosh Error`_.
+
+    .. math:: \text{LogCoshError} = \log\left(\frac{\exp(\hat{y} - y) + \exp(\hat{y - y})}{2}\right)
+
+    Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.
+
+    Args:
+        num_outputs: Number of outputs in multioutput setting
+        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
+
+    Example (single output regression)::
+        >>> from torchmetrics import LogCoshError
+        >>> preds = torch.tensor([3.0, 5.0, 2.5, 7.0])
+        >>> target = torch.tensor([2.5, 5.0, 4.0, 8.0])
+        >>> log_cosh_error = LogCoshError()
+        >>> log_cosh_error(preds, target)
+        tensor(0.3523)
+
+    Example (multi output regression)::
+        >>> from torchmetrics import LogCoshError
+        >>> preds = torch.tensor([[3.0, 5.0, 1.2], [-2.1, 2.5, 7.0]])
+        >>> target = torch.tensor([[2.5, 5.0, 1.3], [0.3, 4.0, 8.0]])
+        >>> log_cosh_error = LogCoshError(num_outputs=3)
+        >>> log_cosh_error(preds, target)
+        tensor([0.9176, 0.4277, 0.2194])
+    """
+
+    is_differentiable = True
+    higher_is_better = False
+    full_state_update = False
+    sum_log_cosh_error: Tensor
+    total: Tensor
+
+    def __init__(self, num_outputs: int = 1, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+
+        if not isinstance(num_outputs, int) and num_outputs < 1:
+            raise ValueError(f"Expected argument `num_outputs` to be an int larger than 0, but got {num_outputs}")
+        self.num_outputs = num_outputs
+        self.add_state("sum_log_cosh_error", default=torch.zeros(num_outputs), dist_reduce_fx="sum")
+        self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
+
+    def update(self, preds: Tensor, target: Tensor) -> None:
+        """Update state with predictions and targets.
+
+        Args:
+            preds: estimated labels with shape ``(batch_size,)`` or `(batch_size, num_outputs)``
+            target: ground truth labels with shape ``(batch_size,)`` or `(batch_size, num_outputs)``
+
+        Raises:
+            ValueError:
+                If ``preds`` or ``target`` has multiple outputs when ``num_outputs=1``
+        """
+        sum_log_cosh_error, n_obs = _log_cosh_error_update(preds, target, self.num_outputs)
+        self.sum_log_cosh_error += sum_log_cosh_error
+        self.total += n_obs
+
+    def compute(self) -> Tensor:
+        """Compute LogCosh error over state."""
+        return _log_cosh_error_compute(self.sum_log_cosh_error, self.total)