Fix dataset id

src/entropice/dataset.py

@@ -12,7 +12,8 @@ Naming conventions:
 """
 
 import hashlib
-from dataclasses import dataclass, field
+import json
+from dataclasses import asdict, dataclass, field
 from typing import Literal
 
 import geopandas as gpd
@@ -21,7 +22,6 @@ import seaborn as sns
 import xarray as xr
 from rich import pretty, traceback
 from sklearn import set_config
-from stopuhr import stopwatch
 
 import entropice.paths
 
@@ -33,7 +33,6 @@ set_config(array_api_dispatch=True)
 sns.set_theme("talk", "whitegrid")
 
 
-@stopwatch.f("Get ERA5 tempus", print_kwargs=["temporal"])
 def _get_era5_tempus(df: pd.DataFrame, temporal: Literal["yearly", "seasonal", "shoulder"]):
     if temporal == "yearly":
         return df.index.get_level_values("time").year
@@ -69,27 +68,10 @@ class DatasetEnsemble:
 
     def id(self):
         return hashlib.blake2b(
-            str(
-                (
-                    self.grid,
-                    self.level,
-                    self.target,
-                    frozenset(self.members),
-                    frozenset(
-                        (member, frozenset(dim_filters.items()))
-                        for member, dim_filters in self.dimension_filters.items()
-                    ),
-                    frozenset(
-                        (member, frozenset(var_filters)) for member, var_filters in self.variable_filters.items()
-                    ),
-                    self.filter_target,
-                    self.add_lonlat,
-                )
-            ).encode("utf-8"),
+            json.dumps(asdict(self), sort_keys=True).encode("utf-8"),
             digest_size=16,
         ).hexdigest()
 
-    @stopwatch.f("Read member data", print_kwargs=["member", "lazy"])
     def _read_member(self, member: L2Dataset, targets: gpd.GeoDataFrame, lazy: bool = False) -> xr.Dataset:
         if member == "AlphaEarth":
             store = entropice.paths.get_embeddings_store(grid=self.grid, level=self.level)
@@ -133,7 +115,6 @@ class DatasetEnsemble:
             ds.load()
         return ds
 
-    @stopwatch("Loading targets")
     def _read_target(self) -> gpd.GeoDataFrame:
         if self.target == "darts_rts":
             target_store = entropice.paths.get_darts_rts_file(grid=self.grid, level=self.level)
@@ -157,7 +138,6 @@ class DatasetEnsemble:
 
         return targets
 
-    @stopwatch.f("Prepare ERA5 data", print_kwargs=["temporal"])
     def _prep_era5(
         self, targets: gpd.GeoDataFrame, temporal: Literal["yearly", "seasonal", "shoulder"]
     ) -> pd.DataFrame:
@@ -172,7 +152,6 @@ class DatasetEnsemble:
             era5_df.columns = [f"era5_{var}_{t}_{agg}" for var, t, agg in era5_df.columns]
         return era5_df
 
-    @stopwatch("Prepare embeddings data")
     def _prep_embeddings(self, targets: gpd.GeoDataFrame) -> pd.DataFrame:
         embeddings = self._read_member("AlphaEarth", targets)["embeddings"]
         embeddings_df = embeddings.to_dataframe(name="value")
@@ -180,7 +159,6 @@ class DatasetEnsemble:
         embeddings_df.columns = [f"embeddings_{agg}_{band}_{year}" for year, agg, band in embeddings_df.columns]
         return embeddings_df
 
-    @stopwatch("Prepare arcticdem data")
     def _prep_arcticdem(self, targets: gpd.GeoDataFrame) -> pd.DataFrame:
         arcticdem = self._read_member("ArcticDEM", targets)
 
@@ -208,12 +186,11 @@ class DatasetEnsemble:
             n_cols += n_cols_member
         print(f"=== Total number of features in dataset: {n_cols}")
 
-    @stopwatch("Create dataset")
     def create(self, cache_mode: Literal["n", "o", "r"] = "r") -> pd.DataFrame:
         # n: no cache, o: overwrite cache, r: read cache if exists
         cache_file = entropice.paths.get_dataset_cache(self.id())
         if cache_mode == "r" and cache_file.exists():
-            dataset = pd.read_parquet(cache_file)
+            dataset = gpd.read_parquet(cache_file)
             print(
                 f"Loaded cached dataset from {cache_file} with {len(dataset)} samples"
                 f" and {len(dataset.columns)} features."
@@ -232,7 +209,6 @@ class DatasetEnsemble:
             else:
                 raise NotImplementedError(f"Member {member} not implemented.")
 
-        with stopwatch("Combine datasets"):
         dataset = targets.set_index("cell_id").join(member_dfs)
         print(f"Prepared dataset with {len(dataset)} samples and {len(dataset.columns)} features.")