def test_model_selector(tmp_path):
n_regions = 1
n_plants = 1
n_products = 2
target_col_name = "Quantity"
persist_path = os.path.join(tmp_path, "results")
df = generate_multiple_tsdata(
n_dates=200, n_regions=n_regions, n_plants=n_plants, n_products=n_products
)
ms = ModelSelector(frequency="D", horizon=1, country_code_column="Country")
assert ms.horizon == 1
ms.create_gridsearch(
n_splits=1,
prophet_models=True,
sklearn_models=False,
sklearn_models_optimize_for_horizon=False,
autosarimax_models=False,
tbats_models=False,
exp_smooth_models=False,
average_ensembles=False,
stacking_ensembles=False,
exog_cols=["Raining"],
)
assert hasattr(ms, "grid_search")
ms.add_model_to_gridsearch(get_sklearn_wrapper(LinearRegression))
ms.select_model(
df=df,
target_col_name=target_col_name,
partition_columns=["Region", "Plant", "Product"],
)
assert len(ms.results) == n_regions * n_plants * n_products
assert len(ms.partitions) == n_regions * n_plants * n_products
ms.persist_results(persist_path)
print(ms.partitions)
ms_load = load_model_selector(folder_path=persist_path)
# we do not ensure the same order of results and partitions after loading, thus checking they are all there
assert all([partition in ms_load.partitions for partition in ms.partitions])
# TODO redefine __eq__ for ModelSelectorResult to str(MSR).__dict__?
assert all(
[
str(ms_load.get_result_for_partition(partition).__dict__)
== str(ms.get_result_for_partition(partition).__dict__)
for partition in ms.partitions
]
)
assert ms.horizon == ms_load.horizon
assert ms.frequency == ms_load.frequency
def test_model_selector(tmp_path):
n_regions = 1
n_plants = 1
n_products = 2
target_col_name = "Quantity"
persist_path = os.path.join(tmp_path, "results")
df = generate_multiple_tsdata(n_dates=200,
n_regions=n_regions,
n_plants=n_plants,
n_products=n_products)
ms = ModelSelector(frequency="D", horizon=1, country_code_column="Country")
with pytest.raises(ValueError):
ms.results
with pytest.raises(ValueError):
ms.partitions
with pytest.raises(ValueError):
ms.stored_path
with pytest.raises(ValueError):
ms.get_result_for_partition(partition="non existing partition")
assert ms.horizon == 1
ms.create_gridsearch(
n_splits=1,
prophet_models=True,
sklearn_models=False,
sklearn_models_optimize_for_horizon=False,
autosarimax_models=False,
tbats_models=False,
exp_smooth_models=False,
average_ensembles=False,
stacking_ensembles=False,
exog_cols=["Raining"],
)
assert hasattr(ms, "grid_search")
assert isinstance(ms.grid_search.estimator.named_steps["holiday"],
HolidayTransformer)
ms.add_model_to_gridsearch(get_sklearn_wrapper(LinearRegression))
ms.select_model(
df=df,
target_col_name=target_col_name,
partition_columns=["Region", "Plant", "Product"],
)
assert len(ms.results) == n_regions * n_plants * n_products
assert len(ms.partitions) == n_regions * n_plants * n_products
ms.persist_results(persist_path)
print(ms.partitions)
ms_load = load_model_selector(folder_path=persist_path)
# we do not ensure the same order of results and partitions after loading,
# thus checking they are all there
assert all(
[partition in ms_load.partitions for partition in ms.partitions])
# TODO redefine __eq__ for ModelSelectorResult to str(MSR).__dict__?
assert all([
str(ms_load.get_result_for_partition(partition).__dict__) == str(
ms.get_result_for_partition(partition).__dict__)
for partition in ms.partitions
])
assert ms.horizon == ms_load.horizon
assert ms.frequency == ms_load.frequency
ms.plot_best_wrapper_classes()
ms.plot_results()
assert "ModelSelector" in repr(ms)
assert "ModelSelectorResults" in repr(ms)
assert "ModelSelectorResult" in repr(ms.results[0])
with pytest.raises(ValueError):
ms.results[0].persist(attribute_name="non_existing_attribute")
assert ms.results[0].cv_splits_overlap is False
ms.results[0].plot_error()