expand test column dtypes to full scale

tests/integration/release/test_release.py

@@ -2,14 +2,13 @@
 
 from typing import Any
 
+import numpy as np
 import pandas as pd
-import pytest
 from _pytest.fixtures import FixtureRequest
 from vivarium_testing_utils import FuzzyChecker
 
 from pseudopeople.dataset import Dataset
 from pseudopeople.schema_entities import COLUMNS, DATASET_SCHEMAS
-from tests.constants import DATASET_GENERATION_FUNCS
 from tests.integration.conftest import IDX_COLS, _get_common_datasets, get_unnoised_data
 from tests.utilities import (
     initialize_dataset_with_sample,
@@ -49,6 +48,25 @@ def test_row_noising_omit_row_or_do_not_respond(
     run_omit_row_or_do_not_respond_tests(dataset_name, config, original_data, noised_data)
 
 
+def test_column_dtypes(
+    unnoised_dataset: Dataset,
+    noised_data: pd.DataFrame,
+    dataset_name: str,
+    config: dict[str, Any],
+) -> None:
+    """Tests that column dtypes are as expected"""
+    for col_name in noised_data.columns:
+        col = COLUMNS.get_column(col_name)
+        expected_dtype = col.dtype_name
+        if expected_dtype == np.dtype(object):
+            # str dtype is 'object'
+            # Check that they are actually strings and not some other
+            # type of object.
+            actual_types = noised_data[col.name].dropna().apply(type)
+            assert (actual_types == str).all(), actual_types.unique()
+        assert noised_data[col.name].dtype == expected_dtype
+
+
 def test_unnoised_id_cols(dataset_name: str, request: FixtureRequest) -> None:
     """Tests that all datasets retain unnoised simulant_id and household_id
     (except for SSA which does not include household_id)

tests/integration/test_interface.py

@@ -175,55 +175,6 @@ def test_seed_behavior(
     assert not noised_data.equals(noised_data_different_seed)
 
 
-@pytest.mark.parametrize(
-    "dataset_name",
-    [
-        DATASET_SCHEMAS.census.name,
-        DATASET_SCHEMAS.acs.name,
-        DATASET_SCHEMAS.cps.name,
-        DATASET_SCHEMAS.ssa.name,
-        DATASET_SCHEMAS.tax_w2_1099.name,
-        DATASET_SCHEMAS.wic.name,
-        DATASET_SCHEMAS.tax_1040.name,
-    ],
-)
-@pytest.mark.parametrize(
-    "engine",
-    [
-        "pandas",
-        "dask",
-    ],
-)
-def test_column_dtypes(
-    dataset_name: str, engine: str, config: dict[str, Any], request: FixtureRequest
-) -> None:
-    """Tests that column dtypes are as expected"""
-    if "TODO" in dataset_name:
-        pytest.skip(reason=dataset_name)
-
-    if engine == "dask":
-        generation_function = DATASET_GENERATION_FUNCS[dataset_name]
-        noised_data = generation_function(
-            seed=SEED,
-            year=None,
-            config=config,
-            engine=engine,
-        ).compute()
-    else:
-        noised_data = request.getfixturevalue(f"noised_sample_data_{dataset_name}")
-
-    for col_name in noised_data.columns:
-        col = COLUMNS.get_column(col_name)
-        expected_dtype = col.dtype_name
-        if expected_dtype == np.dtype(object):
-            # str dtype is 'object'
-            # Check that they are actually strings and not some other
-            # type of object.
-            actual_types = noised_data[col.name].dropna().apply(type)
-            assert (actual_types == str).all(), actual_types.unique()
-        assert noised_data[col.name].dtype == expected_dtype
-
-
 @pytest.mark.skip(reason="TODO: Implement duplication row noising")
 @pytest.mark.parametrize(
     "dataset_name",