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pvnet/models/base_model.py

@@ -10,16 +10,15 @@
 import pandas as pd
 import torch
 import torch.nn.functional as F
-import yaml
 import wandb
+import yaml
 from huggingface_hub import ModelCard, ModelCardData, PyTorchModelHubMixin
 from huggingface_hub.constants import CONFIG_NAME, PYTORCH_WEIGHTS_NAME
 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 ocf_datapipes.batch 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,
@@ -275,9 +274,7 @@ def __init__(
         self.weighted_losses = WeightedLosses(forecast_length=self.forecast_len_30)
 
         self._accumulated_metrics = MetricAccumulator()
-        self._accumulated_batches = BatchAccumulator(
-            key_to_keep=self._target_key
-        )
+        self._accumulated_batches = BatchAccumulator(key_to_keep=self._target_key)
         self._accumulated_y_hat = PredAccumulator()
 
     @property
@@ -434,14 +431,14 @@ def _training_accumulate_log(self, batch, batch_idx, losses, y_hat):
             # We only create the figure every 8 log steps
             # This was reduced as it was creating figures too often
             if grad_batch_num % (8 * self.trainer.log_every_n_steps) == 0:
-               fig = plot_batch_forecasts(
-                   batch,
-                   y_hat,
-                   batch_idx,
-                   quantiles=self.output_quantiles,
-                   key_to_plot=self._target_key_name,
-               )
-               fig.savefig("latest_logged_train_batch.png")
+                fig = plot_batch_forecasts(
+                    batch,
+                    y_hat,
+                    batch_idx,
+                    quantiles=self.output_quantiles,
+                    key_to_plot=self._target_key_name,
+                )
+                fig.savefig("latest_logged_train_batch.png")
 
     def training_step(self, batch, batch_idx):
         """Run training step"""
@@ -477,18 +474,18 @@ def validation_step(self, batch: dict, batch_idx):
         # for each step in the forecast horizon
         # This is needed for the custom plot
         # And needs to be in order of step
-        x_values = [int(k.split("_")[-1].split("/")[0]) for k in logged_losses.keys() if "MAE_horizon/step" in k]
+        x_values = [
+            int(k.split("_")[-1].split("/")[0])
+            for k in logged_losses.keys()
+            if "MAE_horizon/step" in k
+        ]
         y_values = []
         for x in x_values:
             y_values.append(logged_losses[f"MAE_horizon/step_{x:03}/val"])
         per_step_losses = [[x, y] for (x, y) in zip(x_values, y_values)]
         table = wandb.Table(data=per_step_losses, columns=["timestep", "MAE"])
         wandb.log(
-            {
-                "mae_vs_timestep": wandb.plot.line(
-                    table, "timestep", "MAE", title="MAE vs Timestep"
-                )
-            }
+            {"mae_vs_timestep": wandb.plot.line(table, "timestep", "MAE", title="MAE vs Timestep")}
         )
 
         self.log_dict(
@@ -503,9 +500,7 @@ def validation_step(self, batch: dict, batch_idx):
             # Store these temporarily under self
             if not hasattr(self, "_val_y_hats"):
                 self._val_y_hats = PredAccumulator()
-                self._val_batches = BatchAccumulator(
-                    key_to_keep=self._target_key_name
-                )
+                self._val_batches = BatchAccumulator(key_to_keep=self._target_key_name)
 
             self._val_y_hats.append(y_hat)
             self._val_batches.append(batch)
@@ -515,16 +510,16 @@ def validation_step(self, batch: dict, batch_idx):
                 batch = self._val_batches.flush()
 
                 fig = plot_batch_forecasts(
-                   batch,
-                   y_hat,
-                   quantiles=self.output_quantiles,
-                   key_to_plot=self._target_key_name,
+                    batch,
+                    y_hat,
+                    quantiles=self.output_quantiles,
+                    key_to_plot=self._target_key_name,
                 )
 
                 self.logger.experiment.log(
-                   {
-                       f"val_forecast_samples/batch_idx_{accum_batch_num}": wandb.Image(fig),
-                   }
+                    {
+                        f"val_forecast_samples/batch_idx_{accum_batch_num}": wandb.Image(fig),
+                    }
                 )
                 del self._val_y_hats
                 del self._val_batches

pvnet/models/utils.py

@@ -99,9 +99,12 @@ def __bool__(self):
 
     # @staticmethod
     def _filter_batch_dict(self, d):
-        keep_keys = (
-            [BatchKey[self.key_to_keep], BatchKey[f"{self.key_to_keep}_id"], BatchKey[f"{self.key_to_keep}_t0_idx"], BatchKey[f"{self.key_to_keep}_time_utc"]]
-        )
+        keep_keys = [
+            BatchKey[self.key_to_keep],
+            BatchKey[f"{self.key_to_keep}_id"],
+            BatchKey[f"{self.key_to_keep}_t0_idx"],
+            BatchKey[f"{self.key_to_keep}_time_utc"],
+        ]
         return {k: v for k, v in d.items() if k in keep_keys}
 
     def append(self, batch: dict[BatchKey, list[torch.Tensor]]):

pvnet/utils.py

@@ -255,7 +255,7 @@ def _get_numpy(key):
     y = batch[y_key][:, :, 0].cpu().numpy()  # Select the one it is trained on
     y_hat = y_hat.cpu().numpy()
     gsp_ids = batch[y_id_key][:, 0].cpu().numpy().squeeze()
-    t0_idx = int(batch[t0_idx_key])
+    int(batch[t0_idx_key])
     plotting_name = key_to_plot.upper()
 
     gsp_ids = batch[y_id_key].cpu().numpy().squeeze()