slice 7: flat categoricals + ColumnSpec.categorical flag

Adds seven flat categorical features (dwelling_type, tenure,
transaction_type, property_type, built_form, region_code,
country_code) emitted as raw strings. New ColumnSpec.categorical
bool tells the parquet writer to cast these to pd.Categorical at the
I/O boundary, keeping pandas out of the domain/schema module.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

packages/domain/src/domain/ml/schema.py

@@ -9,11 +9,18 @@ from dataclasses import dataclass
 
 @dataclass(frozen=True)
 class ColumnSpec:
-    """Specification of a single column in the EPC ML training dataset."""
+    """Specification of a single column in the EPC ML training dataset.
+
+    `categorical=True` signals that the column carries a categorical value (raw
+    strings emitted by the transform) and should be cast to `pd.Categorical` at
+    parquet write time. The schema module stays pandas-free; the cast happens at
+    the I/O boundary in `services/ml_training_data/`.
+    """
 
     dtype: type
     nullable: bool = True
     description: str = ""
+    categorical: bool = False
 
 
 @dataclass(frozen=True)

packages/domain/src/domain/ml/tests/_fixtures.py

@@ -41,13 +41,20 @@ def make_minimal_sap10_epc(
     percent_draughtproofed: Optional[int] = None,
     energy_rating_average: Optional[int] = None,
     environmental_impact_current: Optional[int] = None,
+    dwelling_type: str = "Mid-terrace house",
+    tenure: str = "1",
+    transaction_type: str = "1",
+    property_type: Optional[str] = None,
+    built_form: Optional[str] = None,
+    region_code: Optional[str] = None,
+    country_code: Optional[str] = None,
 ) -> EpcPropertyData:
     """Construct a minimal valid SAP10 EpcPropertyData with parametrisable targets."""
     return EpcPropertyData(
-        dwelling_type="Mid-terrace house",
+        dwelling_type=dwelling_type,
         inspection_date=date(2025, 6, 1),
-        tenure="1",
-        transaction_type="1",
+        tenure=tenure,
+        transaction_type=transaction_type,
         address_line_1="1 Test Street",
         postcode="A1 1AA",
         post_town="Testtown",
@@ -93,6 +100,10 @@ def make_minimal_sap10_epc(
         percent_draughtproofed=percent_draughtproofed,
         energy_rating_average=energy_rating_average,
         environmental_impact_current=environmental_impact_current,
+        property_type=property_type,
+        built_form=built_form,
+        region_code=region_code,
+        country_code=country_code,
         renewable_heat_incentive=RenewableHeatIncentive(
             space_heating_kwh=space_heating_kwh,
             water_heating_kwh=water_heating_kwh,

packages/domain/src/domain/ml/tests/test_transform.py

@@ -239,3 +239,67 @@ def test_to_row_extracts_boolean_and_optional_int_features() -> None:
     assert row["percent_draughtproofed"] == 100
     assert row["energy_rating_average"] == 60
     assert row["environmental_impact_current"] == 72
+
+
+_NULLABLE_CATEGORICAL_FEATURES: tuple[str, ...] = (
+    "property_type",
+    "built_form",
+    "region_code",
+    "country_code",
+)
+
+
+_NON_NULLABLE_CATEGORICAL_FEATURES: tuple[str, ...] = (
+    "dwelling_type",
+    "tenure",
+    "transaction_type",
+)
+
+
+def test_schema_advertises_categorical_features() -> None:
+    # Arrange
+    transform = EpcMlTransform()
+
+    # Act
+    schema = transform.schema()
+
+    # Assert
+    for feature_name in _NULLABLE_CATEGORICAL_FEATURES:
+        assert feature_name in schema.feature_columns, feature_name
+        column = schema.feature_columns[feature_name]
+        assert column.dtype is str
+        assert column.categorical is True
+        assert column.nullable is True
+    for feature_name in _NON_NULLABLE_CATEGORICAL_FEATURES:
+        assert feature_name in schema.feature_columns, feature_name
+        column = schema.feature_columns[feature_name]
+        assert column.dtype is str
+        assert column.categorical is True
+        assert column.nullable is False
+
+
+def test_to_row_extracts_categorical_features() -> None:
+    # Arrange
+    epc = make_minimal_sap10_epc(
+        energy_rating_current=82,
+        dwelling_type="End-terrace house",
+        tenure="3",
+        transaction_type="8",
+        property_type="0",
+        built_form="2",
+        region_code="6",
+        country_code="ENG",
+    )
+    transform = EpcMlTransform()
+
+    # Act
+    row = transform.to_row(epc)
+
+    # Assert
+    assert row["dwelling_type"] == "End-terrace house"
+    assert row["tenure"] == "3"
+    assert row["transaction_type"] == "8"
+    assert row["property_type"] == "0"
+    assert row["built_form"] == "2"
+    assert row["region_code"] == "6"
+    assert row["country_code"] == "ENG"

packages/domain/src/domain/ml/transform.py

@@ -92,6 +92,49 @@ _FEATURE_COLUMNS: dict[str, ColumnSpec] = {
         nullable=True,
         description="Environmental impact rating; separate from energy efficiency SAP score.",
     ),
+    # Categoricals — emitted as raw strings; downstream casts to pd.Categorical
+    "dwelling_type": ColumnSpec(
+        dtype=str,
+        nullable=False,
+        categorical=True,
+        description="Free-form SAP dwelling-type description, e.g. 'Mid-terrace house'.",
+    ),
+    "tenure": ColumnSpec(
+        dtype=str,
+        nullable=False,
+        categorical=True,
+        description="SAP tenure code, stringified int (e.g. '1' owner-occupied).",
+    ),
+    "transaction_type": ColumnSpec(
+        dtype=str,
+        nullable=False,
+        categorical=True,
+        description="SAP transaction type code, stringified int.",
+    ),
+    "property_type": ColumnSpec(
+        dtype=str,
+        nullable=True,
+        categorical=True,
+        description="SAP property type code, stringified int.",
+    ),
+    "built_form": ColumnSpec(
+        dtype=str,
+        nullable=True,
+        categorical=True,
+        description="SAP built-form code, stringified int.",
+    ),
+    "region_code": ColumnSpec(
+        dtype=str,
+        nullable=True,
+        categorical=True,
+        description="SAP region code (stringified int) — coarse climate / fuel-rate proxy.",
+    ),
+    "country_code": ColumnSpec(
+        dtype=str,
+        nullable=True,
+        categorical=True,
+        description="ISO-style country code, e.g. 'ENG', 'WAL', 'EAW'.",
+    ),
 }
 
 
@@ -188,6 +231,14 @@ class EpcMlTransform:
             "percent_draughtproofed": epc.percent_draughtproofed,
             "energy_rating_average": epc.energy_rating_average,
             "environmental_impact_current": epc.environmental_impact_current,
+            # Features — categoricals (raw strings; cast at parquet write time)
+            "dwelling_type": epc.dwelling_type,
+            "tenure": epc.tenure,
+            "transaction_type": epc.transaction_type,
+            "property_type": epc.property_type,
+            "built_form": epc.built_form,
+            "region_code": epc.region_code,
+            "country_code": epc.country_code,
             # Targets
             "sap_score": epc.energy_rating_current,
             "co2_emissions": epc.co2_emissions_current,