R2 score metric

catalyst/callbacks/metrics/__init__.py

@@ -17,6 +17,8 @@
 
 from catalyst.callbacks.metrics.functional_metric import FunctionalMetricCallback
 
+from catalyst.callbacks.metrics.r2_squared import R2SquaredCallback
+
 from catalyst.callbacks.metrics.recsys import (
     HitrateCallback,
     MAPCallback,

catalyst/callbacks/metrics/r2_squared.py

@@ -0,0 +1,75 @@
+from catalyst.callbacks.metric import LoaderMetricCallback
+from catalyst.metrics._r2_squared import R2Squared
+
+
+class R2SquaredCallback(LoaderMetricCallback):
+    """R2 Squared  metric callback.
+
+    Args:
+        input_key: input key to use for r2squared calculation, specifies our ``y_true``.
+        target_key: output key to use for r2squared calculation, specifies our ``y_pred``.
+        prefix: metric prefix
+        suffix: metric suffix
+
+    Examples:
+
+    .. code-block:: python
+
+        import torch
+        from torch.utils.data import DataLoader, TensorDataset
+        from catalyst import dl
+
+        # data
+        num_samples, num_features = int(1e4), int(1e1)
+        X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
+        dataset = TensorDataset(X, y)
+        loader = DataLoader(dataset, batch_size=32, num_workers=1)
+        loaders = {"train": loader, "valid": loader}
+
+        # model, criterion, optimizer, scheduler
+        model = torch.nn.Linear(num_features, 1)
+        criterion = torch.nn.MSELoss()
+        optimizer = torch.optim.Adam(model.parameters())
+        scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [3, 6])
+
+        # model training
+        runner = dl.SupervisedRunner()
+        runner.train(
+            model=model,
+            criterion=criterion,
+            optimizer=optimizer,
+            scheduler=scheduler,
+            loaders=loaders,
+            logdir="./logdir",
+            valid_loader="valid",
+            valid_metric="loss",
+            minimize_valid_metric=True,
+            num_epochs=8,
+            verbose=True,
+            callbacks=[
+                      dl.R2SquaredCallback(input_key="logits", target_key="targets")
+            ]
+        )
+
+    .. note::
+        Please follow the `minimal examples`_ sections for more use cases.
+
+        .. _`minimal examples`: https://github.com/catalyst-team/catalyst#minimal-examples
+    """
+
+    def __init__(
+        self,
+        input_key: str,
+        target_key: str,
+        prefix: str = None,
+        suffix: str = None,
+    ):
+        """Init."""
+        super().__init__(
+            metric=R2Squared(prefix=prefix, suffix=suffix),
+            input_key=input_key,
+            target_key=target_key,
+        )
+
+
+__all__ = ["R2SquaredCallback"]

catalyst/metrics/__init__.py

@@ -28,6 +28,7 @@
 from catalyst.metrics._map import MAPMetric
 from catalyst.metrics._mrr import MRRMetric
 from catalyst.metrics._ndcg import NDCGMetric
+from catalyst.metrics._r2_squared import R2Squared
 from catalyst.metrics._segmentation import (
     RegionBasedMetric,
     IOUMetric,

catalyst/metrics/_r2_squared.py

@@ -0,0 +1,64 @@
+from typing import Optional
+
+import torch
+
+from catalyst.metrics._metric import ICallbackLoaderMetric
+
+
+class R2Squared(ICallbackLoaderMetric):
+    """This metric accumulates r2 score along loader
+
+    Args:
+        compute_on_call: if True, allows compute metric's value on call
+        prefix: metric prefix
+        suffix: metric suffix
+    """
+
+    def __init__(
+        self,
+        compute_on_call: bool = True,
+        prefix: Optional[str] = None,
+        suffix: Optional[str] = None,
+    ) -> None:
+        """Init R2Squared"""
+        super().__init__(compute_on_call=compute_on_call, prefix=prefix, suffix=suffix)
+        self.metric_name = f"{self.prefix}r2squared{self.suffix}"
+        self.num_examples = 0
+        self.delta_sum = 0
+        self.y_sum = 0
+        self.y_sq_sum = 0
+
+    def reset(self, num_batches: int, num_samples: int) -> None:
+        """
+        Reset metrics fields
+        """
+        self.num_examples = 0
+        self.delta_sum = 0
+        self.y_sum = 0
+        self.y_sq_sum = 0
+
+    def update(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> None:
+        """
+        Update accumulated data with new batch
+        """
+        self.num_examples += len(y_true)
+        self.delta_sum += torch.sum(torch.pow(y_pred - y_true, 2))
+        self.y_sum += torch.sum(y_true)
+        self.y_sq_sum += torch.sum(torch.pow(y_true, 2))
+
+    def compute(self) -> torch.Tensor:
+        """
+        Return accumulated metric
+        """
+        return 1 - self.delta_sum / (self.y_sq_sum - (self.y_sum ** 2) / self.num_examples)
+
+    def compute_key_value(self) -> torch.Tensor:
+        """
+        Return key-value
+        """
+        r2squared = self.compute()
+        output = {self.metric_name: r2squared}
+        return output
+
+
+__all__ = ["R2Squared"]

catalyst/metrics/functional/__init__.py

@@ -27,6 +27,7 @@
 from catalyst.metrics.functional._mrr import reciprocal_rank, mrr
 from catalyst.metrics.functional._ndcg import dcg, ndcg
 from catalyst.metrics.functional._precision import precision
+from catalyst.metrics.functional._r2_squared import r2_squared
 from catalyst.metrics.functional._recall import recall
 from catalyst.metrics.functional._segmentation import (
     iou,

catalyst/metrics/functional/_r2_squared.py

@@ -0,0 +1,50 @@
+from typing import Sequence
+
+import torch
+
+
+def r2_squared(outputs: torch.Tensor, targets: torch.Tensor) -> Sequence[torch.Tensor]:
+    """
+    Computes regression r2 squared.
+
+    Args:
+        outputs: model outputs
+            with shape [bs; 1]
+        targets: ground truth
+            with shape [bs; 1]
+
+    Returns:
+        float of computed r2 squared
+
+    Examples:
+
+    .. code-block:: python
+
+        import torch
+        from catalyst import metrics
+        metrics.r2_squared(
+            outputs=torch.tensor([0, 1, 2]),
+            targets=torch.tensor([0, 1, 2]),
+        )
+        # tensor([1.])
+
+
+    .. code-block:: python
+
+        import torch
+        from catalyst import metrics
+        metrics.r2_squared(
+            outputs=torch.tensor([2.5, 0.0, 2, 8]),
+            targets=torch.tensor([3, -0.5, 2, 7]),
+        )
+        # tensor([0.9486])
+    """
+    total_sum_of_squares = torch.sum(
+        torch.pow(targets.float() - torch.mean(targets.float()), 2)
+    ).view(-1)
+    residual_sum_of_squares = torch.sum(torch.pow(targets.float() - outputs.float(), 2)).view(-1)
+    output = 1 - residual_sum_of_squares / total_sum_of_squares
+    return output
+
+
+__all__ = ["r2_squared"]

docs/api/metrics.rst

@@ -163,6 +163,13 @@ RecSys – NDCGMetric
     :undoc-members:
     :show-inheritance:
 
+Regression – R2Squared
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autoclass:: catalyst.metrics._r2_squared.R2Squared
+    :exclude-members: __init__
+    :undoc-members:
+    :show-inheritance:
+
 Segmentation – RegionBasedMetric
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. autoclass:: catalyst.metrics._segmentation.RegionBasedMetric
@@ -272,6 +279,13 @@ Precision
     :undoc-members:
     :show-inheritance:
 
+R2Squared
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. automodule:: catalyst.metrics.functional._r2_squared
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 Recall
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. automodule:: catalyst.metrics.functional._recall

tests/catalyst/metrics/functional/test_r2_squared.py

@@ -0,0 +1,16 @@
+# flake8: noqa
+import numpy as np
+
+import torch
+
+from catalyst.metrics.functional._r2_squared import r2_squared
+
+
+def test_r2_squared():
+    """
+    Tests for catalyst.metrics.r2_squared metric.
+    """
+    y_true = torch.tensor([3, -0.5, 2, 7])
+    y_pred = torch.tensor([2.5, 0.0, 2, 8])
+    val = r2_squared(y_pred, y_true)
+    assert torch.isclose(val, torch.Tensor([0.9486]))

tests/catalyst/metrics/test_r2squared.py

@@ -0,0 +1,83 @@
+# flake8: noqa
+from typing import Dict, Iterable, Union
+
+import pytest
+
+import torch
+
+from catalyst.metrics._r2_squared import R2Squared
+
+
+@pytest.mark.parametrize(
+    "outputs,targets,true_values",
+    (
+        (
+            torch.Tensor([2.5, 0.0, 2, 8]),
+            torch.Tensor([3, -0.5, 2, 7]),
+            {
+                "r2squared": torch.Tensor([0.9486]),
+            },
+        ),
+    ),
+)
+def test_r2_squared(
+    outputs: torch.Tensor,
+    targets: torch.Tensor,
+    true_values: Dict[str, torch.Tensor],
+) -> None:
+    """
+    Test r2 squared metric
+
+    Args:
+        outputs: tensor of outputs
+        targets: tensor of targets
+        true_values: true metric values
+    """
+    metric = R2Squared()
+    metric.update(y_pred=outputs, y_true=targets)
+    metrics = metric.compute_key_value()
+    for key in true_values.keys():
+        assert torch.isclose(true_values[key], metrics[key])
+
+
+@pytest.mark.parametrize(
+    "outputs_list,targets_list,true_values",
+    (
+        (
+            (
+                torch.Tensor([2.5, 0.0, 2, 8]),
+                torch.Tensor([2.5, 0.0, 2, 8]),
+                torch.Tensor([2.5, 0.0, 2, 8]),
+                torch.Tensor([2.5, 0.0, 2, 8]),
+            ),
+            (
+                torch.Tensor([3, -0.5, 2, 7]),
+                torch.Tensor([3, -0.5, 2, 7]),
+                torch.Tensor([3, -0.5, 2, 7]),
+                torch.Tensor([3, -0.5, 2, 7]),
+            ),
+            {
+                "r2squared": torch.Tensor([0.9486]),
+            },
+        ),
+    ),
+)
+def test_r2_squared_update(
+    outputs_list: Iterable[torch.Tensor],
+    targets_list: Iterable[torch.Tensor],
+    true_values: Dict[str, torch.Tensor],
+):
+    """
+    Test r2 squared metric computation
+
+    Args:
+        outputs_list: list of outputs
+        targets_list: list of targets
+        true_values: true metric values
+    """
+    metric = R2Squared()
+    for outputs, targets in zip(outputs_list, targets_list):
+        metric.update(y_pred=outputs, y_true=targets)
+    metrics = metric.compute_key_value()
+    for key in true_values.keys():
+        assert torch.isclose(true_values[key], metrics[key])

tests/pipelines/test_regression.py

@@ -41,6 +41,7 @@ def train_experiment(device, engine=None):
             minimize_valid_metric=True,
             num_epochs=1,
             verbose=False,
+            callbacks=[dl.R2SquaredCallback(input_key="logits", target_key="targets")],
         )