- default cv_folds for ts is set to 3

- pep8 changes
aimclub · Sep 6, 2022 · 52c15e9 · 52c15e9
1 parent 43a3064
commit 52c15e9
Show file tree

Hide file tree

Showing 3 changed files with 6 additions and 7 deletions.
diff --git a/test/unit/composer/test_history.py b/test/unit/composer/test_history.py
@@ -166,7 +166,7 @@ def test_collect_intermediate_metric(pipeline: Pipeline, input_data: InputData,
 @pytest.mark.parametrize("cv_generator, data",
                          [(partial(tabular_cv_generator, folds=5),
                            get_classification_data()),
-                          (partial(ts_cv_generator, folds=5, validation_blocks=2),
+                          (partial(ts_cv_generator, folds=3, validation_blocks=2),
                            get_ts_data()[0])])
 def test_cv_generator_works_stable(cv_generator, data):
     """ Test if ts cv generator works stable (always return same folds) """

diff --git a/test/unit/validation/test_table_cv.py b/test/unit/validation/test_table_cv.py
@@ -44,7 +44,7 @@ def get_classification_data():
 def test_cv_multiple_metrics_evaluated_correct(classification_dataset):
     pipeline = sample_pipeline()
 
-    cv_folds = partial(tabular_cv_generator, classification_dataset, folds=3)
+    cv_folds = partial(tabular_cv_generator, classification_dataset, folds=5)
     metrics = [ClassificationMetricsEnum.ROCAUC_penalty,
                ClassificationMetricsEnum.accuracy,
                ClassificationMetricsEnum.logloss]
@@ -106,7 +106,7 @@ def test_composer_with_cv_optimization_correct():
                                                          secondary=available_model_types,
                                                          timeout=timedelta(minutes=0.2),
                                                          num_of_generations=2, cv_folds=5,
-                                                         logging_level_opt=logging.CRITICAL+1,
+                                                         logging_level_opt=logging.CRITICAL + 1,
                                                          show_progress=False)
 
     builder = ComposerBuilder(task).with_requirements(composer_requirements).with_metrics(metric_function)
@@ -130,7 +130,7 @@ def test_cv_api_correct():
                        'num_of_generations': 1,
                        'preset': 'fast_train',
                        'cv_folds': 2,
-                       'logging_level_opt': logging.CRITICAL+1,
+                       'logging_level_opt': logging.CRITICAL + 1,
                        'show_progress': False}
     dataset_to_compose, dataset_to_validate = train_test_data_setup(get_classification_data())
     model = Fedot(problem='classification', logging_level=logging.INFO, **composer_params)

diff --git a/test/unit/validation/test_time_series_cv.py b/test/unit/validation/test_time_series_cv.py
@@ -3,7 +3,6 @@
 
 import numpy as np
 import pytest
-from sklearn.metrics import mean_absolute_error
 
 from examples.advanced.time_series_forecasting.composing_pipelines import get_available_operations
 from fedot.api.main import Fedot
@@ -126,7 +125,7 @@ def test_composer_cv_correct():
         timeout=datetime.timedelta(seconds=5),
         cv_folds=folds,
         validation_blocks=validation_blocks,
-        logging_level_opt=logging.CRITICAL+1,
+        logging_level_opt=logging.CRITICAL + 1,
         show_progress=False)
 
     init_pipeline = get_simple_ts_pipeline()
@@ -152,7 +151,7 @@ def test_api_cv_correct():
                        'cv_folds': folds,
                        'num_of_generations': 1,
                        'validation_blocks': validation_blocks,
-                       'logging_level_opt': logging.CRITICAL+1,
+                       'logging_level_opt': logging.CRITICAL + 1,
                        'show_progress': False}
     task_parameters = TsForecastingParams(forecast_length=forecast_len)