Update explained variance metric

pytorch_lightning/metrics/metric.py

@@ -234,3 +234,8 @@ def __setstate__(self, state):
         self.__dict__.update(state)
         self.update = self._wrap_update(self.update)
         self.compute = self._wrap_compute(self.compute)
+
+    def _check_same_shape(self, pred: torch.Tensor, target: torch.Tensor):
+        """ Check that predictions and target have the same shape, else raise error """
+        if pred.shape != target.shape:
+            raise RuntimeError('Predictions and targets are expected to have the same shape')

pytorch_lightning/metrics/regression/explained_variance.py

@@ -1,13 +1,40 @@
 import torch
-from typing import Any, Callable, Optional, Union
+from typing import Any, Optional
 
 from pytorch_lightning.metrics.metric import Metric
+from pytorch_lightning.utilities import rank_zero_warn
 
 
 class ExplainedVariance(Metric):
     """
     Computes explained variance.
 
+    Forward accepts
+
+    - ``preds`` (float tensor): ``(N,)`` or ``(N, ...)`` (multioutput)
+    - ``target`` (long tensor): ``(N,)`` or ``(N, ...)`` (multioutput)
+
+    In the case of multioutput, as default the variances will be uniformly
+    averaged over the additional dimensions. Please see argument `multioutput`
+    for changing this behavior.
+
+    Args:
+        multioutput:
+            Defines aggregation in the case of multiple output scores. Can be one
+            of the following strings (default is `'uniform_average'`.):
+
+            * `'raw_values'` returns full set of scores
+            * `'uniform_average'` scores are uniformly averaged
+            * `'variance_weighted'` scores are weighted by their individual variances
+
+        compute_on_step:
+            Forward only calls ``update()`` and return None if this is set to False. default: True
+        ddp_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 ExplainedVariance
@@ -17,11 +44,16 @@ class ExplainedVariance(Metric):
         >>> explained_variance(preds, target)
         tensor(0.9572)
 
-
+        >>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]])
+        >>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]])
+        >>> explained_variance = ExplainedVariance(multioutput='raw_values')
+        >>> explained_variance(preds, target)
+        tensor([0.9677, 1.0000])
     """
 
     def __init__(
         self,
+        multioutput: str = 'uniform_average',
         compute_on_step: bool = True,
         ddp_sync_on_step: bool = False,
         process_group: Optional[Any] = None,
@@ -31,10 +63,19 @@ def __init__(
             ddp_sync_on_step=ddp_sync_on_step,
             process_group=process_group,
         )
-
+        allowed_multioutput = ('raw_values', 'uniform_average', 'variance_weighted')
+        if multioutput not in allowed_multioutput:
+            raise ValueError(
+                f'Invalid input to argument `multioutput`. Choose one of the following: {allowed_multioutput}'
+            )
+        self.multioutput = multioutput
         self.add_state("y", default=[], dist_reduce_fx=None)
         self.add_state("y_pred", default=[], dist_reduce_fx=None)
 
+        rank_zero_warn('Metric `ExplainedVariance` will save all targets and'
+                       ' predictions in buffer. For large datasets this may lead'
+                       ' to large memory footprint.')
+
     def update(self, preds: torch.Tensor, target: torch.Tensor):
         """
         Update state with predictions and targets.
@@ -43,6 +84,7 @@ def update(self, preds: torch.Tensor, target: torch.Tensor):
             preds: Predictions from model
             target: Ground truth values
         """
+        self._check_same_shape(preds, target)
         self.y.append(target)
         self.y_pred.append(preds)
 
@@ -59,5 +101,20 @@ def compute(self):
         y_true_avg = torch.mean(y_true, dim=0)
         denominator = torch.mean((y_true - y_true_avg) ** 2, dim=0)
 
-        # TODO: multioutput
-        return 1.0 - torch.mean(numerator / denominator)
+        # Take care of division by zero
+        nonzero_numerator = numerator != 0
+        nonzero_denominator = denominator != 0
+        valid_score = nonzero_numerator & nonzero_denominator
+        output_scores = torch.ones_like(y_diff_avg)
+        output_scores[valid_score] = 1.0 - (numerator[valid_score] / denominator[valid_score])
+        output_scores[nonzero_numerator & ~nonzero_denominator] = 0.
+
+        # Decide what to do in multioutput case
+        # Todo: allow user to pass in tensor with weights
+        if self.multioutput == 'raw_values':
+            return output_scores
+        if self.multioutput == 'uniform_average':
+            return torch.mean(output_scores)
+        if self.multioutput == 'variance_weighted':
+            denom_sum = torch.sum(denominator)
+            return torch.sum(denominator / denom_sum * output_scores)

pytorch_lightning/metrics/regression/mean_absolute_error.py

@@ -50,8 +50,7 @@ def update(self, preds: torch.Tensor, target: torch.Tensor):
             preds: Predictions from model
             target: Ground truth values
         """
-        assert preds.shape == target.shape, \
-            'Predictions and targets are expected to have the same shape'
+        self._check_same_shape(preds, target)
         abs_error = torch.abs(preds - target)
 
         self.sum_abs_error += torch.sum(abs_error)

pytorch_lightning/metrics/regression/mean_squared_error.py

@@ -51,8 +51,7 @@ def update(self, preds: torch.Tensor, target: torch.Tensor):
             preds: Predictions from model
             target: Ground truth values
         """
-        assert preds.shape == target.shape, \
-            'Predictions and targets are expected to have the same shape'
+        self._check_same_shape(preds, target)
         squared_error = torch.pow(preds - target, 2)
 
         self.sum_squared_error += torch.sum(squared_error)

pytorch_lightning/metrics/regression/mean_squared_log_error.py

@@ -51,8 +51,7 @@ def update(self, preds: torch.Tensor, target: torch.Tensor):
             preds: Predictions from model
             target: Ground truth values
         """
-        assert preds.shape == target.shape, \
-            'Predictions and targets are expected to have the same shape'
+        self._check_same_shape(preds, target)
         squared_log_error = torch.pow(torch.log1p(preds) - torch.log1p(target), 2)
 
         self.sum_squared_log_error += torch.sum(squared_log_error)

tests/metrics/classification/test_accuracy.py

@@ -1,15 +1,13 @@
 import os
 import pytest
 import torch
-import os
 import numpy as np
 from collections import namedtuple
 
 from pytorch_lightning.metrics.classification.accuracy import Accuracy
 from sklearn.metrics import accuracy_score
 
-from tests.metrics.utils import compute_batch, setup_ddp
-from tests.metrics.utils import NUM_BATCHES, NUM_PROCESSES, BATCH_SIZE
+from tests.metrics.utils import compute_batch, NUM_BATCHES, BATCH_SIZE
 
 torch.manual_seed(42)
 

tests/metrics/regression/test_explained_variance.py

@@ -6,8 +6,7 @@
 from pytorch_lightning.metrics.regression import ExplainedVariance
 from sklearn.metrics import explained_variance_score
 
-from tests.metrics.utils import compute_batch, setup_ddp
-from tests.metrics.utils import NUM_BATCHES, NUM_PROCESSES, BATCH_SIZE
+from tests.metrics.utils import compute_batch, NUM_BATCHES, BATCH_SIZE
 
 torch.manual_seed(42)
 
@@ -40,12 +39,28 @@ def _multi_target_sk_metric(preds, target, sk_fn=explained_variance_score):
 
 @pytest.mark.parametrize("ddp", [True, False])
 @pytest.mark.parametrize("ddp_sync_on_step", [True, False])
+@pytest.mark.parametrize("multioutput", ['raw_values', 'uniform_average', 'variance_weighted'])
 @pytest.mark.parametrize(
     "preds, target, sk_metric",
     [
         (_single_target_inputs.preds, _single_target_inputs.target, _single_target_sk_metric),
         (_multi_target_inputs.preds, _multi_target_inputs.target, _multi_target_sk_metric),
     ],
 )
-def test_explained_variance(ddp, ddp_sync_on_step, preds, target, sk_metric):
-    compute_batch(preds, target, ExplainedVariance, sk_metric, ddp_sync_on_step, ddp)
+def test_explained_variance(ddp, ddp_sync_on_step, multioutput, preds, target, sk_metric):
+    compute_batch(
+        preds,
+        target,
+        ExplainedVariance,
+        partial(sk_metric, sk_fn=partial(explained_variance_score, multioutput=multioutput)),
+        ddp_sync_on_step,
+        ddp,
+        metric_args=dict(multioutput=multioutput),
+    )
+
+
+def test_error_on_different_shape(metric_class=ExplainedVariance):
+    metric = metric_class()
+    with pytest.raises(RuntimeError,
+                       match='Predictions and targets are expected to have the same shape'):
+        metric(torch.randn(100,), torch.randn(50,))

tests/metrics/regression/test_mean_error.py

@@ -6,8 +6,7 @@
 from pytorch_lightning.metrics.regression import MeanSquaredError, MeanAbsoluteError, MeanSquaredLogError
 from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_squared_log_error
 
-from tests.metrics.utils import compute_batch, setup_ddp
-from tests.metrics.utils import NUM_BATCHES, NUM_PROCESSES, BATCH_SIZE
+from tests.metrics.utils import compute_batch, NUM_BATCHES, BATCH_SIZE
 
 torch.manual_seed(42)
 
@@ -57,3 +56,11 @@ def _multi_target_sk_metric(preds, target, sk_fn=mean_squared_error):
 )
 def test_mean_error(ddp, ddp_sync_on_step, preds, target, sk_metric, metric_class, sk_fn):
     compute_batch(preds, target, metric_class, partial(sk_metric, sk_fn=sk_fn), ddp_sync_on_step, ddp)
+
+
+@pytest.mark.parametrize("metric_class", [MeanSquaredError, MeanAbsoluteError, MeanSquaredLogError])
+def test_error_on_different_shape(metric_class):
+    metric = metric_class()
+    with pytest.raises(RuntimeError,
+                       match='Predictions and targets are expected to have the same shape'):
+        metric(torch.randn(100,), torch.randn(50,))

tests/metrics/utils.py

@@ -1,23 +1,39 @@
-import torch
-import numpy as np
 import os
 import sys
 import pytest
 import pickle
+from typing import Callable
+
+import torch
+import numpy as np
+
+from pytorch_lightning.metrics import Metric
 
 NUM_PROCESSES = 2
 NUM_BATCHES = 10
 BATCH_SIZE = 16
 
 
 def setup_ddp(rank, world_size):
+    """ Setup ddp enviroment """
     os.environ["MASTER_ADDR"] = 'localhost'
     os.environ['MASTER_PORT'] = '8088'
     torch.distributed.init_process_group("gloo", rank=rank, world_size=world_size)
 
 
-def _compute_batch(rank, preds, target, metric_class, sk_metric, ddp_sync_on_step, worldsize=1, metric_args={}):
-
+def _compute_batch(rank: int,
+                   preds: torch.Tensor,
+                   target: torch.Tensor,
+                   metric_class: Metric,
+                   sk_metric: Callable,
+                   ddp_sync_on_step: bool,
+                   worldsize: int = 1,
+                   metric_args: dict = {}
+                   ):
+    """ Utility function doing the actual comparison between lightning metric
+        and reference metric
+    """
+    # Instanciate lightning metric
     metric = metric_class(compute_on_step=True, ddp_sync_on_step=ddp_sync_on_step, **metric_args)
 
     # verify metrics work after being loaded from pickled state
@@ -52,7 +68,27 @@ def _compute_batch(rank, preds, target, metric_class, sk_metric, ddp_sync_on_ste
     assert np.allclose(result.numpy(), sk_result)
 
 
-def compute_batch(preds, target, metric_class, sk_metric, ddp_sync_on_step, ddp=False, metric_args={}):
+def compute_batch(preds: torch.Tensor,
+                  target: torch.Tensor,
+                  metric_class: Metric,
+                  sk_metric: Callable,
+                  ddp_sync_on_step: bool,
+                  ddp: bool = False,
+                  metric_args: dict = {}
+                  ):
+    """ Utility function for comparing the result between a lightning class
+        metric and another metric (often sklearns)
+
+        Args:
+            preds: prediction tensor
+            target: target tensor
+            metric_class: lightning metric class to test
+            sk_metric: function to compare with
+            ddp_sync_on_step: bool, determine if values should be reduce on step
+            ddp: bool, determine if test should run in ddp mode
+            metric_args: dict, additional kwargs that are use when instanciating
+                the lightning metric
+    """
     if ddp:
         if sys.platform == "win32":
             pytest.skip("DDP not supported on windows")