Pre-commit changes

bluecast/config/base_classes.py

@@ -16,9 +16,8 @@ class BaseClassExperimentTracker(ABC):
     def add_results(
         self,
         experiment_id: Union[int, str, float],
-        experiment_name: Union[int, str, float],
         score_category: Literal["hyperparameter_tuning", "oof_score"],
-        training_configs: TrainingConfig,
+        training_config: TrainingConfig,
         model_parameters: Dict[
             Union[str, int, float, None], Union[str, int, float, None]
         ],

bluecast/config/training_config.py

@@ -7,8 +7,8 @@
 """
 from typing import Dict, Optional
 
-from pydantic.dataclasses import dataclass
 from pydantic import BaseModel
+from pydantic.dataclasses import dataclass
 
 
 class Config:

bluecast/tests/test_experiment_tracker.py

@@ -1,48 +1,68 @@
 import pytest
+
 from bluecast.config.training_config import TrainingConfig
 from bluecast.experimentation.tracking import ExperimentTracker
 
+
 @pytest.fixture
 def experiment_tracker():
     return ExperimentTracker()
 
+
 def test_add_results(experiment_tracker):
     # Add some sample data
     experiment_id = 1
     score_category = "hyperparameter_tuning"
-    training_config = TrainingConfig()  # You may need to create a valid TrainingConfig instance
+    training_config = (
+        TrainingConfig()
+    )  # You may need to create a valid TrainingConfig instance
     model_parameters = {"param1": 1, "param2": "abc"}
     eval_scores = 0.95
     metric_used = "accuracy"
     metric_higher_is_better = True
 
     experiment_tracker.add_results(
-        experiment_id, score_category, training_config,
-        model_parameters, eval_scores, metric_used, metric_higher_is_better
+        experiment_id,
+        score_category,
+        training_config,
+        model_parameters,
+        eval_scores,
+        metric_used,
+        metric_higher_is_better,
     )
 
     # Check if the data was added correctly
     assert experiment_tracker.experiment_id == [1]
     assert experiment_tracker.score_category == ["hyperparameter_tuning"]
-    assert experiment_tracker.training_configs == [training_config.model_dump(mode="json")]
+    assert experiment_tracker.training_configs == [
+        training_config.model_dump(mode="json")
+    ]
     assert experiment_tracker.model_parameters == [model_parameters]
     assert experiment_tracker.eval_scores == [0.95]
     assert experiment_tracker.metric_used == ["accuracy"]
     assert experiment_tracker.metric_higher_is_better == [True]
 
+
 def test_retrieve_results_as_df(experiment_tracker):
     # Add some sample data
     experiment_id = 1
     score_category = "hyperparameter_tuning"
-    training_config = TrainingConfig()  # You may need to create a valid TrainingConfig instance
+    training_config = (
+        TrainingConfig()
+    )  # You may need to create a valid TrainingConfig instance
     model_parameters = {"param1": 1, "param2": "abc"}
     eval_scores = 0.95
     metric_used = "accuracy"
     metric_higher_is_better = True
 
     experiment_tracker.add_results(
-        experiment_id, score_category, training_config,
-        model_parameters, eval_scores, metric_used, metric_higher_is_better
+        experiment_id,
+        score_category,
+        training_config,
+        model_parameters,
+        eval_scores,
+        metric_used,
+        metric_higher_is_better,
     )
 
     # Retrieve the results as a DataFrame