add metric to the notorious tests

aimclub · Oct 12, 2023 · 5b44396 · 5b44396
1 parent 9434106
commit 5b44396
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Showing 3 changed files with 4 additions and 3 deletions.
diff --git a/examples/advanced/time_series_forecasting/exogenous.py b/examples/advanced/time_series_forecasting/exogenous.py
@@ -76,6 +76,7 @@ def run_exogenous_experiment(path_to_file, len_forecast=250, with_exog=True, vis
                   task_params=task.task_params,
                   timeout=10,
                   initial_assumption=pipeline,
+                  metric=['mae'],
                   available_operations=['lagged', 'ridge', 'exog_ts', 'arima', 'knnreg', 'rfr', 'svr'],
                   max_pipeline_fit_time=2,
                   n_jobs=-1)

diff --git a/examples/simple/time_series_forecasting/api_forecasting.py b/examples/simple/time_series_forecasting/api_forecasting.py
@@ -7,7 +7,7 @@
 from fedot.core.data.data import InputData
 from fedot.core.data.data_split import train_test_data_setup
 from fedot.core.repository.dataset_types import DataTypesEnum
-from fedot.core.repository.tasks import TsForecastingParams, Task, TaskTypesEnum
+from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
 
 logging.raiseExceptions = False
 
@@ -41,7 +41,7 @@ def run_ts_forecasting_example(dataset='australia', horizon: int = 30, timeout:
                                    TsForecastingParams(forecast_length=horizon)).task_params,
                   timeout=timeout,
                   n_jobs=-1,
-                  metric='mase',
+                  metric=['mase', 'mae', 'mape', 'rmse'],
                   with_tuning=with_tuning,
                   cv_folds=2, preset='fast_train')
 

diff --git a/test/integration/api/test_main_api.py b/test/integration/api/test_main_api.py
@@ -157,7 +157,7 @@ def data_with_binary_features_and_categorical_target():
 ])
 def test_api_predict_correct(task_type, metric_name):
     train_data, test_data, _ = get_dataset(task_type)
-    model = Fedot(problem=task_type, **TESTS_MAIN_API_DEFAULT_PARAMS)
+    model = Fedot(problem=task_type, metric=[metric_name], **TESTS_MAIN_API_DEFAULT_PARAMS)
     fedot_model = model.fit(features=train_data)
     prediction = model.predict(features=test_data)
     metric = model.get_metrics(metric_names=metric_name, rounding_order=5)