Remove properties

pvnet_summation/models/base_model.py

@@ -75,6 +75,22 @@
         self._accumulated_y_sum = PredAccumulator()
         self._accumulated_times = PredAccumulator()
 
+        self.use_quantile_regression = self.output_quantiles is not None
+
+        if self.use_quantile_regression:
+            self.num_output_features = self.forecast_len_30 * len(self.output_quantiles)
+        else:
+            self.num_output_features = self.forecast_len_30
+
+        if self.pvnet_model.use_quantile_regression:
+            self.pvnet_output_shape = (
+                317,
+                self.pvnet_model.forecast_len,
+                len(self.pvnet_model.output_quantiles),
+            )
+        else:
+            self.pvnet_output_shape = (317, self.pvnet_model.forecast_len)
+
     def predict_pvnet_batch(self, batch):
         """Use PVNet model to create predictions for batch"""
         gsp_batches = []
@@ -92,14 +108,6 @@
 
         return (y_hat * x["effective_capacity"]).sum(dim=1)
 
-    @property
-    def pvnet_output_shape(self):
-        """Return the expected shape of the PVNet outputs"""
-        if self.pvnet_model.use_quantile_regression:
-            return (317, self.pvnet_model.forecast_len_30, len(self.pvnet_model.output_quantiles))
-        else:
-            return (317, self.pvnet_model.forecast_len_30)
-
     def _training_accumulate_log(self, batch_idx, losses, y_hat, y, y_sum, times):
         """Internal function to accumulate training batches and log results.
 

requirements.txt

@@ -1,6 +1,5 @@
 ocf_datapipes==3.2.*
-pvnet==2.6.*
-ocf_ml_metrics==0.0.*
+pvnet==3.0.*
 numpy
 pandas
 matplotlib
@@ -9,20 +8,15 @@ ipykernel
 h5netcdf
 torch>=2.0.0
 lightning>=2.0.1
-torchdata
 pytest
 pytest-cov
 typer
 sqlalchemy
-jedi
 fsspec[s3]
-tables
-tilemapbase
-testcontainers
 wandb
 tensorboard
 tqdm
-rich
 omegaconf
 hydra-core
 python-dotenv
+huggingface-hub==0.20.*

tests/conftest.py

@@ -138,7 +138,7 @@ def model_kwargs():
     # These kwargs define the pvnet model which the summation model uses
     kwargs = dict(
         model_name="openclimatefix/pvnet_v2",
-        model_version="22e577100d55787eb2547d701275b9bb48f7bfa0",
+        model_version="4203e12e719efd93da641c43d2e38527648f4915",
     )
     return kwargs