chore: type errors in bigframes.ml tests (#702)

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Fixes internal #340890167, 340891711, 340876028🦕

Author: ashleyxuu

tests/system/large/ml/test_decomposition.py

@@ -64,10 +64,10 @@ def test_decomposition_configure_fit_score_predict(
     reloaded_model = model.to_gbq(
         f"{dataset_id}.temp_configured_pca_model", replace=True
     )
-    # TODO(b/340876028): fix type error
+    assert reloaded_model._bqml_model is not None
     assert (
         f"{dataset_id}.temp_configured_pca_model"
-        in reloaded_model._bqml_model.model_name  # type: ignore
+        in reloaded_model._bqml_model.model_name
     )
     assert reloaded_model.n_components == 3
 
@@ -120,10 +120,10 @@ def test_decomposition_configure_fit_score_predict_params(
     reloaded_model = model.to_gbq(
         f"{dataset_id}.temp_configured_pca_model", replace=True
     )
-    # TODO(b/340876028): fix type error
+    assert reloaded_model._bqml_model is not None
     assert (
         f"{dataset_id}.temp_configured_pca_model"
-        in reloaded_model._bqml_model.model_name  # type: ignore
+        in reloaded_model._bqml_model.model_name
     )
     assert reloaded_model.n_components == 5
     assert reloaded_model.svd_solver == "RANDOMIZED"
@@ -139,10 +139,10 @@ def test_decomposition_configure_fit_load_float_component(
     reloaded_model = model.to_gbq(
         f"{dataset_id}.temp_configured_pca_model", replace=True
     )
-    # TODO(b/340876028): fix type error
+    assert reloaded_model._bqml_model is not None
     assert (
         f"{dataset_id}.temp_configured_pca_model"
-        in reloaded_model._bqml_model.model_name  # type: ignore
+        in reloaded_model._bqml_model.model_name
     )
     assert reloaded_model.n_components == 0.2
 
@@ -157,9 +157,9 @@ def test_decomposition_configure_fit_load_none_component(
     reloaded_model = model.to_gbq(
         f"{dataset_id}.temp_configured_pca_model", replace=True
     )
-    # TODO(b/340876028): fix type error
+    assert reloaded_model._bqml_model is not None
     assert (
         f"{dataset_id}.temp_configured_pca_model"
-        in reloaded_model._bqml_model.model_name  # type: ignore
+        in reloaded_model._bqml_model.model_name
     )
     assert reloaded_model.n_components == 7

tests/system/large/ml/test_forecasting.py

@@ -119,9 +119,9 @@ def test_arima_plus_model_fit_params(time_series_df_default_index, dataset_id):
 
     # save, load to ensure configuration was kept
     reloaded_model = model.to_gbq(f"{dataset_id}.temp_arima_plus_model", replace=True)
-    # TODO(b/340891711): fix type error
+    assert reloaded_model._bqml_model is not None
     assert (
-        f"{dataset_id}.temp_arima_plus_model" in reloaded_model._bqml_model.model_name  # type: ignore
+        f"{dataset_id}.temp_arima_plus_model" in reloaded_model._bqml_model.model_name
     )
 
     assert reloaded_model.horizon == 100

tests/system/large/ml/test_linear_model.py

@@ -41,9 +41,9 @@ def test_linear_regression_configure_fit_score(penguins_df_default_index, datase
 
     # save, load, check parameters to ensure configuration was kept
     reloaded_model = model.to_gbq(f"{dataset_id}.temp_configured_model", replace=True)
-    # TODO(b/340890167): fix type error
+    assert reloaded_model._bqml_model is not None
     assert (
-        f"{dataset_id}.temp_configured_model" in reloaded_model._bqml_model.model_name  # type: ignore
+        f"{dataset_id}.temp_configured_model" in reloaded_model._bqml_model.model_name
     )
     assert reloaded_model.optimize_strategy == "NORMAL_EQUATION"
     assert reloaded_model.fit_intercept is True
@@ -94,9 +94,9 @@ def test_linear_regression_customized_params_fit_score(
 
     # save, load, check parameters to ensure configuration was kept
     reloaded_model = model.to_gbq(f"{dataset_id}.temp_configured_model", replace=True)
-    # TODO(b/340890167): fix type error
+    assert reloaded_model._bqml_model is not None
     assert (
-        f"{dataset_id}.temp_configured_model" in reloaded_model._bqml_model.model_name  # type: ignore
+        f"{dataset_id}.temp_configured_model" in reloaded_model._bqml_model.model_name
     )
     assert reloaded_model.optimize_strategy == "BATCH_GRADIENT_DESCENT"
     assert reloaded_model.fit_intercept is False
@@ -141,10 +141,10 @@ def test_logistic_regression_configure_fit_score(penguins_df_default_index, data
     reloaded_model = model.to_gbq(
         f"{dataset_id}.temp_configured_logistic_reg_model", replace=True
     )
-    # TODO(b/340890167): fix type error
+    assert reloaded_model._bqml_model is not None
     assert (
         f"{dataset_id}.temp_configured_logistic_reg_model"
-        in reloaded_model._bqml_model.model_name  # type: ignore
+        in reloaded_model._bqml_model.model_name
     )
     assert reloaded_model.fit_intercept is True
     assert reloaded_model.class_weight is None
@@ -187,10 +187,10 @@ def test_logistic_regression_customized_params_fit_score(
     reloaded_model = model.to_gbq(
         f"{dataset_id}.temp_configured_logistic_reg_model", replace=True
     )
-    # TODO(b/340890167): fix type error
+    assert reloaded_model._bqml_model is not None
     assert (
         f"{dataset_id}.temp_configured_logistic_reg_model"
-        in reloaded_model._bqml_model.model_name  # type: ignore
+        in reloaded_model._bqml_model.model_name
     )
     # TODO(garrettwu) optimize_strategy isn't logged in BQML
     # assert reloaded_model.optimize_strategy == "BATCH_GRADIENT_DESCENT"