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()
def seleciona_modelo_horizonte3(dtf_train='',
target_col='',
seed=42,
horizonte=1,
exog_features_list='',
lags=3,
pack=''):
#if horizonte<3:
# lags=horizonte
ms = ModelSelector(
horizon=horizonte,
frequency='D',
country_code_column=
None # 'country' --> deixar None, se nao ocorre erro de execucao
)
ms.create_gridsearch(
sklearn_models=False,
n_splits=2, # 10 cross-validation splits
between_split_lag=None,
sklearn_models_optimize_for_horizon=False,
autosarimax_models=
False, # Autosarimax agora esta funcionando, com pmdarima=1.5.3
prophet_models=
False, # Nao ativar, pois usaremos o NeuralProphet em seu lugar
tbats_models=False, # TBATS funcionando OK (pip install tbats)
exp_smooth_models=False, # exp_smooth funcionando OK
average_ensembles=False, # average_ensembles, funcionando OK
stacking_ensembles=
False, # Nao vamos usar, demora muito e nao da bom resultado
exog_cols=exog_features_list,
# exog_cols=None,
#holidays_days_before=2,
#holidays_days_after=1,
#holidays_bridge_days=True,
)
#ms.add_model_to_gridsearch(NeuralProphetWrapper(exog_cols=exog_features.columns.tolist()))
use_scikit = True
huber, ridge, xgb_sq, xgb_hb = pack
if use_scikit:
if target_col in xgb_hb:
xgb_r = get_sklearn_wrapper(
XGBRegressor, lags=lags,
objective="reg:pseudohubererror") # , random_state=seed))
#xgb_r = get_sklearn_wrapper(XGBRegressor,lags=lags,objective="reg:tweedie") # , random_state=seed))
xgb_r.name = 'XGBRegressor_Huber'
ms.add_model_to_gridsearch(xgb_r)
elif target_col in xgb_sq:
xgb_r = get_sklearn_wrapper(XGBRegressor,
lags=lags) # , random_state=seed))
#xgb_r = get_sklearn_wrapper(XGBRegressor,lags=lags,objective="reg:tweedie") # , random_state=seed))
xgb_r.name = 'XGBRegressor_Squared_Loss'
ms.add_model_to_gridsearch(xgb_r)
elif target_col in ridge:
ridge_r = get_sklearn_wrapper(Ridge, random_state=seed, lags=lags)
ridge_r.name = 'Ridge'
ms.add_model_to_gridsearch(ridge_r)
else:
huber_r = get_sklearn_wrapper(HuberRegressor, max_iter=160)
huber_r.name = 'Huber'
ms.add_model_to_gridsearch(huber_r)
# Method `select_model` is doing majority of the magic for you - it creates forecast for each combination of
# columns specified in `partition_columns` and for each of the time series it will run grid_search mentioned
# above. Optionally once can select list of columns over which the model selection will run in parallel using
# prefect (`parallel_over_columns`).
# Required format for data is Datetime index, unsuprisingly numerical column for `target_col_name` all other
# columns except `partition_columns` will be used as exogenous variables - as additional features for modeling.
ms.select_model(
df=dtf_train,
target_col_name=target_col,
partition_columns=None,
# parallel_over_columns=['Assortment'],
# persist_model_selector_results=False,
# output_path='my_results',
# executor = LocalDaskExecutor(),
)
ms.persist_results('results')
#mlflow.log_metric("score", 0.75)
# ============================ Train model ==================================
result = ms.results[0]
print('Model selection result: \n', str(result))
best_model = result.best_model
return ms, best_model, result