fix: misc. fixes

src/application/t2d/generate_features_and_write_to_disk.py

@@ -548,7 +548,7 @@ def create_unresolved_specs() -> dict[str, list[UnresolvedAnySpec]]:
 
     unresolved_specs["static_predictors"] = [
         UnresolvedStaticSpec(
-            values_lookup_name="sex_female", input_col_name_override="sex_female"
+            values_lookup_name="sex_female", input_col_name_override="sex_female", prefix="pred_"
         )
     ]
 
@@ -564,12 +564,13 @@ def get_unresolved_temporal_predictor_specs() -> list[UnresolvedPredictorSpec]:
     unresolved_temporal_predictor_specs: list[UnresolvedPredictorSpec] = []
 
     unresolved_temporal_predictor_specs += UnresolvedLabPredictorGroupSpec(
-        values_lookup_name=("hba1c",),
+        values_lookup_name=["hba1c"],
         fallback=[np.nan],
         lab_values_to_load=["numerical_and_coerce"],
         interval_days=[9999],
         resolve_multiple_fn_name=["count"],
         allowed_nan_value_prop=allowed_nan_value_prop,
+        output_col_name_override="eval_hba1c_count_within_9999_days",
     ).create_combinations()
 
     unresolved_temporal_predictor_specs += UnresolvedLabPredictorGroupSpec(
@@ -606,21 +607,21 @@ def get_unresolved_temporal_predictor_specs() -> list[UnresolvedPredictorSpec]:
         allowed_nan_value_prop=allowed_nan_value_prop,
     ).create_combinations()
 
-    unresolved_temporal_predictor_specs += UnresolvedPredictorGroupSpec(
-        values_lookup_name=("antipsychotics",),
-        interval_days=interval_days,
-        resolve_multiple_fn_name=resolve_multiple,
-        fallback=[0],
-        allowed_nan_value_prop=allowed_nan_value_prop,
-    ).create_combinations()
-
-    unresolved_temporal_predictor_specs += UnresolvedPredictorGroupSpec(
-        values_lookup_name=["weight_in_kg", "height_in_cm", "bmi"],
-        interval_days=interval_days,
-        resolve_multiple_fn_name=["latest"],
-        fallback=[np.nan],
-        allowed_nan_value_prop=allowed_nan_value_prop,
-    ).create_combinations()
+    # unresolved_temporal_predictor_specs += UnresolvedPredictorGroupSpec(
+    #     values_lookup_name=("antipsychotics",),
+    #     interval_days=interval_days,
+    #     resolve_multiple_fn_name=resolve_multiple,
+    #     fallback=[0],
+    #     allowed_nan_value_prop=allowed_nan_value_prop,
+    # ).create_combinations()
+
+    # unresolved_temporal_predictor_specs += UnresolvedPredictorGroupSpec(
+    #     values_lookup_name=["weight_in_kg", "height_in_cm", "bmi"],
+    #     interval_days=interval_days,
+    #     resolve_multiple_fn_name=["latest"],
+    #     fallback=[np.nan],
+    #     allowed_nan_value_prop=allowed_nan_value_prop,
+    # ).create_combinations()
 
     return unresolved_temporal_predictor_specs
 

src/psycop_feature_generation/timeseriesflattener/feature_spec_objects.py

@@ -1,6 +1,7 @@
 """Templates for feature specifications."""
 
 import itertools
+from abc import abstractmethod
 from typing import Callable, Iterable, Literal, Optional, Sequence, Union
 
 import pandas as pd
@@ -30,13 +31,23 @@ class AnySpec(BaseModel):
     """
 
     values_df: pd.DataFrame
+    feature_name: str
+    prefix: str
+    # Used for column name generation, e.g. pred_<feature_name>.
 
     input_col_name_override: Optional[str] = None
     # An override for the input column name. If None, will attempt
     # to infer it by looking for the only column that doesn't match id_col_name or timestamp_col_name.
 
-    output_col_name_override: Optional[str] = None
-    # If none, will output pred_<input_col_name>
+    def get_col_str(self) -> str:
+        """."""
+        col_str = f"{self.prefix}_{self.feature_name}"
+
+        if isinstance(self, OutcomeSpec):
+            if self.is_dichotomous():
+                col_str += "_dichotomous"
+
+        return col_str
 
 
 class StaticSpec(AnySpec):
@@ -65,7 +76,7 @@ class TemporalSpec(AnySpec):
     allowed_nan_value_prop: float = 0.0
 
     # Input col names
-    prefix: Optional[str] = None
+    prefix: str
     id_col_name: str = "dw_ek_borger"
     timestamp_col_name: str = "timestamp"
 
@@ -87,12 +98,7 @@ def __init__(self, **kwargs):
 
     def get_col_str(self, col_main_override: Optional[str] = None) -> str:
         """."""
-        if self.output_col_name_override:
-            return self.output_col_name_override
-
-        col_main = col_main_override if col_main_override else self.feature_name
-
-        col_str = f"{self.prefix}_{col_main}_within_{self.interval_days}_days_{self.resolve_multiple_fn_name}_fallback_{self.fallback}"
+        col_str = f"{self.prefix}_{self.feature_name}_within_{self.interval_days}_days_{self.resolve_multiple_fn_name}_fallback_{self.fallback}"
 
         if isinstance(self, OutcomeSpec):
             if self.is_dichotomous():
@@ -189,7 +195,7 @@ def create_specs_from_group(
 
     permuted_dicts = create_feature_combinations_from_dict(d=feature_group_spec_dict)
 
-    return [output_class(**d) for d in permuted_dicts]
+    return [output_class(**d) for d in permuted_dicts]  # type: ignore
 
 
 class PredictorGroupSpec(MinGroupSpec):

src/psycop_feature_generation/timeseriesflattener/flattened_dataset.py

@@ -647,7 +647,10 @@ def add_age_and_date_of_birth(
             static_spec=StaticSpec(
                 values_df=id2date_of_birth,
                 input_col_name_override=date_of_birth_col_name,
-                output_col_name_override=date_of_birth_col_name,
+                prefix="eval",
+                # We typically don't want to use date of birth as a predictor,
+                # but might want to use transformations - e.g. "year of birth" or "age at prediction time".
+                feature_name=date_of_birth_col_name,
             ),
         )
 
@@ -695,10 +698,7 @@ def add_static_info(
         else:
             value_col_name = static_spec.input_col_name_override
 
-        if static_spec.output_col_name_override is None:
-            output_col_name = f"pred_{value_col_name}"
-        else:
-            output_col_name = f"{static_spec.output_col_name_override}"
+        output_col_name = f"{static_spec}_{value_col_name}"
 
         df = pd.DataFrame(
             {

src/psycop_feature_generation/timeseriesflattener/unresolved_feature_spec_objects.py

@@ -37,13 +37,20 @@ def resolve_spec(
         # Infer feature_name from values_lookup_name
         kwargs_dict["feature_name"] = kwargs_dict["values_lookup_name"]
 
-        # Remove the attributes that are not allowed in the outcome specs,
+        # Remove the attributes that are not allowed in the resolve_to_class,
         # or which are inferred in the return statement.
+
+        # This implementation is super brittle - whenever a new key is added to
+        # any class that is resolved, but which isn't added to feature_spec_objects,
+        # it breaks. Alternative ideas are very welcome.
+        # We can get around it by allowing extras (e.g. attributes which aren't specified) in the feature_spec_objects,
+        # but that leaves us open to typos.
         for redundant_key in (
             "values_df",
             "resolve_multiple_fn",
             "lab_values_to_load",
             "values_lookup_name",
+            "output_col_name_override",
         ):
             if redundant_key in kwargs_dict:
                 kwargs_dict.pop(redundant_key)
@@ -56,6 +63,10 @@ def resolve_spec(
         elif isinstance(self, UnresolvedStaticSpec):
             resolve_to_class = StaticSpec
 
+            if self.output_col_name_override:
+                kwargs_dict["feature_name"] = self.output_col_name_override
+                kwargs_dict["prefix"] = ""
+
             return resolve_to_class(
                 values_df=str2df[self.values_lookup_name], **kwargs_dict
             )
@@ -162,3 +173,4 @@ def create_combinations(self):
 class UnresolvedStaticSpec(UnresolvedAnySpec):
     """Specification for a static feature, where the df has not been
     resolved."""
+    prefix: str = "pred"