simplify regression modules (#748)

tests/regression/test_mean_error.py

@@ -33,16 +33,14 @@
     mean_squared_error,
     mean_squared_log_error,
 )
-from torchmetrics.functional.regression.symmetric_mean_absolute_percentage_error import (
-    symmetric_mean_absolute_percentage_error,
-)
+from torchmetrics.functional.regression.symmetric_mape import symmetric_mean_absolute_percentage_error
 from torchmetrics.regression import (
     MeanAbsoluteError,
     MeanAbsolutePercentageError,
     MeanSquaredError,
     MeanSquaredLogError,
 )
-from torchmetrics.regression.symmetric_mean_absolute_percentage_error import SymmetricMeanAbsolutePercentageError
+from torchmetrics.regression.symmetric_mape import SymmetricMeanAbsolutePercentageError
 from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_6
 
 seed_all(42)

torchmetrics/classification/__init__.py

@@ -22,7 +22,7 @@
 from torchmetrics.classification.cohen_kappa import CohenKappa  # noqa: F401
 from torchmetrics.classification.confusion_matrix import ConfusionMatrix  # noqa: F401
 from torchmetrics.classification.f_beta import F1, F1Score, FBeta, FBetaScore  # noqa: F401
-from torchmetrics.classification.hamming_distance import HammingDistance  # noqa: F401
+from torchmetrics.classification.hamming import HammingDistance  # noqa: F401
 from torchmetrics.classification.hinge import Hinge, HingeLoss  # noqa: F401
 from torchmetrics.classification.iou import IoU  # noqa: F401
 from torchmetrics.classification.jaccard import JaccardIndex  # noqa: F401

torchmetrics/classification/hamming_distance.py → torchmetrics/classification/hamming.py

@@ -16,7 +16,7 @@
 import torch
 from torch import Tensor, tensor
 
-from torchmetrics.functional.classification.hamming_distance import _hamming_distance_compute, _hamming_distance_update
+from torchmetrics.functional.classification.hamming import _hamming_distance_compute, _hamming_distance_update
 from torchmetrics.metric import Metric
 
 

torchmetrics/functional/__init__.py

@@ -25,7 +25,7 @@
 from torchmetrics.functional.classification.confusion_matrix import confusion_matrix
 from torchmetrics.functional.classification.dice import dice_score
 from torchmetrics.functional.classification.f_beta import f1, f1_score, fbeta, fbeta_score
-from torchmetrics.functional.classification.hamming_distance import hamming_distance
+from torchmetrics.functional.classification.hamming import hamming_distance
 from torchmetrics.functional.classification.hinge import hinge, hinge_loss
 from torchmetrics.functional.classification.iou import iou  # noqa: F401
 from torchmetrics.functional.classification.jaccard import jaccard_index
@@ -47,16 +47,14 @@
 from torchmetrics.functional.pairwise.manhatten import pairwise_manhatten_distance
 from torchmetrics.functional.regression.cosine_similarity import cosine_similarity
 from torchmetrics.functional.regression.explained_variance import explained_variance
-from torchmetrics.functional.regression.mean_absolute_error import mean_absolute_error
-from torchmetrics.functional.regression.mean_absolute_percentage_error import mean_absolute_percentage_error
-from torchmetrics.functional.regression.mean_squared_error import mean_squared_error
-from torchmetrics.functional.regression.mean_squared_log_error import mean_squared_log_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
+from torchmetrics.functional.regression.mse import mean_squared_error
 from torchmetrics.functional.regression.pearson import pearson_corrcoef
 from torchmetrics.functional.regression.r2 import r2_score
 from torchmetrics.functional.regression.spearman import spearman_corrcoef
-from torchmetrics.functional.regression.symmetric_mean_absolute_percentage_error import (
-    symmetric_mean_absolute_percentage_error,
-)
+from torchmetrics.functional.regression.symmetric_mape import symmetric_mean_absolute_percentage_error
 from torchmetrics.functional.regression.tweedie_deviance import tweedie_deviance_score
 from torchmetrics.functional.retrieval.average_precision import retrieval_average_precision
 from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out

torchmetrics/functional/classification/__init__.py

@@ -20,7 +20,7 @@
 from torchmetrics.functional.classification.confusion_matrix import confusion_matrix  # noqa: F401
 from torchmetrics.functional.classification.dice import dice_score  # noqa: F401
 from torchmetrics.functional.classification.f_beta import f1, f1_score, fbeta, fbeta_score  # noqa: F401
-from torchmetrics.functional.classification.hamming_distance import hamming_distance  # noqa: F401
+from torchmetrics.functional.classification.hamming import hamming_distance  # noqa: F401
 from torchmetrics.functional.classification.hinge import hinge, hinge_loss  # noqa: F401
 from torchmetrics.functional.classification.jaccard import jaccard_index  # noqa: F401
 from torchmetrics.functional.classification.kl_divergence import kl_divergence  # noqa: F401

torchmetrics/functional/regression/__init__.py

@@ -16,12 +16,10 @@
 from torchmetrics.functional.image.ssim import ssim  # noqa: F401
 from torchmetrics.functional.regression.cosine_similarity import cosine_similarity  # noqa: F401
 from torchmetrics.functional.regression.explained_variance import explained_variance  # noqa: F401
-from torchmetrics.functional.regression.mean_absolute_error import mean_absolute_error  # noqa: F401
-from torchmetrics.functional.regression.mean_absolute_percentage_error import (  # noqa: F401
-    mean_absolute_percentage_error,
-)
-from torchmetrics.functional.regression.mean_squared_error import mean_squared_error  # noqa: F401
-from torchmetrics.functional.regression.mean_squared_log_error import mean_squared_log_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
+from torchmetrics.functional.regression.mse import mean_squared_error  # noqa: F401
 from torchmetrics.functional.regression.pearson import pearson_corrcoef  # noqa: F401
 from torchmetrics.functional.regression.r2 import r2_score  # noqa: F401
 from torchmetrics.functional.regression.spearman import spearman_corrcoef  # noqa: F401

torchmetrics/regression/__init__.py

@@ -13,14 +13,12 @@
 # limitations under the License.
 from torchmetrics.regression.cosine_similarity import CosineSimilarity  # noqa: F401
 from torchmetrics.regression.explained_variance import ExplainedVariance  # noqa: F401
-from torchmetrics.regression.mean_absolute_error import MeanAbsoluteError  # noqa: F401
-from torchmetrics.regression.mean_absolute_percentage_error import MeanAbsolutePercentageError  # noqa: F401
-from torchmetrics.regression.mean_squared_error import MeanSquaredError  # noqa: F401
-from torchmetrics.regression.mean_squared_log_error import MeanSquaredLogError  # 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
+from torchmetrics.regression.mse import MeanSquaredError  # noqa: F401
 from torchmetrics.regression.pearson import PearsonCorrCoef, PearsonCorrcoef  # noqa: F401
 from torchmetrics.regression.r2 import R2Score  # noqa: F401
 from torchmetrics.regression.spearman import SpearmanCorrCoef, SpearmanCorrcoef  # noqa: F401
-from torchmetrics.regression.symmetric_mean_absolute_percentage_error import (  # noqa: F401
-    SymmetricMeanAbsolutePercentageError,
-)
+from torchmetrics.regression.symmetric_mape import SymmetricMeanAbsolutePercentageError  # noqa: F401
 from torchmetrics.regression.tweedie_deviance import TweedieDevianceScore  # noqa: F401

torchmetrics/regression/mean_squared_log_error.py → torchmetrics/regression/log_mse.py

@@ -16,10 +16,7 @@
 import torch
 from torch import Tensor, tensor
 
-from torchmetrics.functional.regression.mean_squared_log_error import (
-    _mean_squared_log_error_compute,
-    _mean_squared_log_error_update,
-)
+from torchmetrics.functional.regression.log_mse import _mean_squared_log_error_compute, _mean_squared_log_error_update
 from torchmetrics.metric import Metric
 
 

torchmetrics/regression/mean_absolute_error.py → torchmetrics/regression/mae.py

@@ -16,10 +16,7 @@
 import torch
 from torch import Tensor, tensor
 
-from torchmetrics.functional.regression.mean_absolute_error import (
-    _mean_absolute_error_compute,
-    _mean_absolute_error_update,
-)
+from torchmetrics.functional.regression.mae import _mean_absolute_error_compute, _mean_absolute_error_update
 from torchmetrics.metric import Metric
 
 

torchmetrics/regression/mean_absolute_percentage_error.py → torchmetrics/regression/mape.py

@@ -16,7 +16,7 @@
 import torch
 from torch import Tensor, tensor
 
-from torchmetrics.functional.regression.mean_absolute_percentage_error import (
+from torchmetrics.functional.regression.mape import (
     _mean_absolute_percentage_error_compute,
     _mean_absolute_percentage_error_update,
 )

torchmetrics/regression/mean_squared_error.py → torchmetrics/regression/mse.py

@@ -16,10 +16,7 @@
 import torch
 from torch import Tensor, tensor
 
-from torchmetrics.functional.regression.mean_squared_error import (
-    _mean_squared_error_compute,
-    _mean_squared_error_update,
-)
+from torchmetrics.functional.regression.mse import _mean_squared_error_compute, _mean_squared_error_update
 from torchmetrics.metric import Metric
 
 

torchmetrics/regression/symmetric_mean_absolute_percentage_error.py → torchmetrics/regression/symmetric_mape.py

@@ -16,7 +16,7 @@
 import torch
 from torch import Tensor, tensor
 
-from torchmetrics.functional.regression.symmetric_mean_absolute_percentage_error import (
+from torchmetrics.functional.regression.symmetric_mape import (
     _symmetric_mean_absolute_percentage_error_compute,
     _symmetric_mean_absolute_percentage_error_update,
 )