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Source code for pytorch_lightning.metrics.regression.mean_squared_log_error

# 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.
import torch
from typing import Any, Callable, Optional

from pytorch_lightning.metrics.metric import Metric
from pytorch_lightning.metrics.functional.mean_squared_log_error import (
    _mean_squared_log_error_update,
    _mean_squared_log_error_compute
)


[docs]class MeanSquaredLogError(Metric): r""" Computes `mean squared logarithmic error <https://scikit-learn.org/stable/modules/model_evaluation.html#mean-squared-log-error>`_ (MSLE): .. math:: \text{MSLE} = \frac{1}{N}\sum_i^N (\log_e(1 + y_i) - \log_e(1 + \hat{y_i}))^2 Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions. Args: compute_on_step: Forward only calls ``update()`` and return None if this is set to False. default: True dist_sync_on_step: Synchronize metric state across processes at each ``forward()`` before returning the value at the step. default: False process_group: Specify the process group on which synchronization is called. default: None (which selects the entire world) Example: >>> from pytorch_lightning.metrics import MeanSquaredLogError >>> target = torch.tensor([2.5, 5, 4, 8]) >>> preds = torch.tensor([3, 5, 2.5, 7]) >>> mean_squared_log_error = MeanSquaredLogError() >>> mean_squared_log_error(preds, target) tensor(0.0397) """ def __init__( self, compute_on_step: bool = True, dist_sync_on_step: bool = False, process_group: Optional[Any] = None, dist_sync_fn: Callable = None, ): super().__init__( compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) self.add_state("sum_squared_log_error", default=torch.tensor(0.0), dist_reduce_fx="sum") self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
[docs] def update(self, preds: torch.Tensor, target: torch.Tensor): """ Update state with predictions and targets. Args: preds: Predictions from model target: Ground truth values """ sum_squared_log_error, n_obs = _mean_squared_log_error_update(preds, target) self.sum_squared_log_error += sum_squared_log_error self.total += n_obs
[docs] def compute(self): """ Compute mean squared logarithmic error over state. """ return _mean_squared_log_error_compute(self.sum_squared_log_error, self.total)

© Copyright Copyright (c) 2018-2021, William Falcon et al... Revision c462b274.

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