Issue/rm nowcasting utils

pvnet/models/base_model.py

@@ -17,18 +17,15 @@
 from huggingface_hub.file_download import hf_hub_download
 from huggingface_hub.hf_api import HfApi
 from huggingface_hub.utils._deprecation import _deprecate_positional_args
-from nowcasting_utils.models.loss import WeightedLosses
-from nowcasting_utils.models.metrics import (
-    mae_each_forecast_horizon,
-    mse_each_forecast_horizon,
-)
 from ocf_datapipes.utils.consts import BatchKey
 from ocf_ml_metrics.evaluation.evaluation import evaluation
+from ocf_ml_metrics.metrics.errors import common_metrics
 
 from pvnet.models.utils import (
     BatchAccumulator,
     MetricAccumulator,
     PredAccumulator,
+    WeightedLosses,
 )
 from pvnet.optimizers import AbstractOptimizer
 from pvnet.utils import construct_ocf_ml_metrics_batch_df, plot_batch_forecasts
@@ -373,8 +370,9 @@
             # Take median value for remaining metric calculations
             y_hat = self._quantiles_to_prediction(y_hat)
 
-        mse_each_step = mse_each_forecast_horizon(output=y_hat, target=y)
-        mae_each_step = mae_each_forecast_horizon(output=y_hat, target=y)
+        common_metrics_each_step = common_metrics(predictions=y_hat, targets=y)
+        mse_each_step = common_metrics_each_step["rmse"] ** 2
+        mae_each_step = common_metrics_each_step["mae"]
 
         losses.update({f"MSE_horizon/step_{i:02}": m for i, m in enumerate(mse_each_step)})
         losses.update({f"MAE_horizon/step_{i:02}": m for i, m in enumerate(mae_each_step)})

pvnet/models/utils.py

@@ -1,9 +1,15 @@
 """Utility functions"""
 
+import logging
+import math
+from typing import Optional
+
 import numpy as np
 import torch
 from ocf_datapipes.utils.consts import BatchKey
 
+logger = logging.getLogger(__name__)
+
 
 class PredAccumulator:
     """A class for accumulating y-predictions using grad accumulation and small batch size.
@@ -110,3 +116,58 @@ def flush(self) -> dict[BatchKey, list[torch.Tensor]]:
                 batch[k] = torch.cat(v, dim=0)
         self._batches = {}
         return batch
+
+
+class WeightedLosses:
+    """Class: Weighted loss depending on the forecast horizon."""
+
+    def __init__(self, decay_rate: Optional[int] = None, forecast_length: int = 6):
+        """
+        Want to set up the MSE loss function so the weights only have to be calculated once.
+
+        Args:
+            decay_rate: The weights exponentially decay depending on the 'decay_rate'.
+            forecast_length: The forecast length is needed to make sure the weights sum to 1
+        """
+        self.decay_rate = decay_rate
+        self.forecast_length = forecast_length
+
+        logger.debug(
+            f"Setting up weights with decay rate {decay_rate} and of length {forecast_length}"
+        )
+
+        # set default rate of ln(2) if not set
+        if self.decay_rate is None:
+            self.decay_rate = math.log(2)
+
+        # make weights from decay rate
+        weights = torch.FloatTensor(
+            [math.exp(-self.decay_rate * i) for i in range(0, self.forecast_length)]
+        )
+
+        # normalized the weights, so there mean is 1.
+        # To calculate the loss, we times the weights by the differences between truth
+        # and predictions and then take the mean across all forecast horizons and the batch
+        self.weights = weights / weights.sum() * len(weights)
+
+        # move weights to gpu is needed
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.weights = self.weights.to(device)
+
+    def get_mse_exp(self, output, target):
+        """Loss function weighted MSE"""
+
+        # get the differences weighted by the forecast horizon weights
+        diff_with_weights = self.weights * ((output - target) ** 2)
+
+        # average across batches
+        return torch.mean(diff_with_weights)
+
+    def get_mae_exp(self, output, target):
+        """Loss function weighted MAE"""
+
+        # get the differences weighted by the forecast horizon weights
+        diff_with_weights = self.weights * torch.abs(output - target)
+
+        # average across batches
+        return torch.mean(diff_with_weights)

requirements.txt

@@ -1,5 +1,4 @@
 ocf_datapipes>=2.2.5
-nowcasting_utils
 ocf_ml_metrics
 numpy
 pandas

tests/models/test_utils.py

@@ -0,0 +1,69 @@
+import pytest
+import torch
+
+from pvnet.models.utils import WeightedLosses
+
+
+def test_weight_losses_weights():
+    """Test weighted loss"""
+    forecast_length = 2
+    w = WeightedLosses(forecast_length=forecast_length)
+
+    assert w.weights.cpu().numpy()[0] == pytest.approx(4 / 3)
+    assert w.weights.cpu().numpy()[1] == pytest.approx(2 / 3)
+
+
+def test_mae_exp():
+    """Test MAE exp with weighted loss"""
+    forecast_length = 2
+    w = WeightedLosses(forecast_length=forecast_length)
+
+    output = torch.Tensor([[1, 3], [1, 3]])
+    target = torch.Tensor([[1, 5], [1, 9]])
+
+    loss = w.get_mae_exp(output=output, target=target)
+
+    # 0.5((1-1)*2/3 + (5-3)*1/3) + 0.5((1-1)*2/3 + (9-3)*1/3) = 1/3 + 3/3
+    assert loss == pytest.approx(4 / 3)
+
+
+def test_mse_exp():
+    """Test MSE exp with weighted loss"""
+    forecast_length = 2
+    w = WeightedLosses(forecast_length=forecast_length)
+
+    output = torch.Tensor([[1, 3], [1, 3]])
+    target = torch.Tensor([[1, 5], [1, 9]])
+
+    loss = w.get_mse_exp(output=output, target=target)
+
+    # 0.5((1-1)^2*2/3 + (5-3)^2*1/3) + 0.5((1-1)^2*2/3 + (9-3)^2*1/3) = 2/3 + 18/3
+    assert loss == pytest.approx(20 / 3)
+
+
+def test_mae_exp_rand():
+    """Test MAE exp with weighted loss  with random tensors"""
+    forecast_length = 6
+    batch_size = 32
+
+    w = WeightedLosses(forecast_length=6)
+
+    output = torch.randn(batch_size, forecast_length)
+    target = torch.randn(batch_size, forecast_length)
+
+    loss = w.get_mae_exp(output=output, target=target)
+    assert loss > 0
+
+
+def test_mse_exp_rand():
+    """Test MSE exp with weighted loss  with random tensors"""
+    forecast_length = 6
+    batch_size = 32
+
+    w = WeightedLosses(forecast_length=6)
+
+    output = torch.randn(batch_size, forecast_length)
+    target = torch.randn(batch_size, forecast_length)
+
+    loss = w.get_mse_exp(output=output, target=target)
+    assert loss > 0