Update ReadMe and docstrings

README.md

@@ -13,9 +13,7 @@
 [![python](https://img.shields.io/badge/Python-3.10-3776AB.svg?style=flat&logo=python&logoColor=white)](https://www.python.org)
 [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
 
-A lightweight and fast auto-ml library. This is the successor of the
-e2eml automl library. While e2eml tried to cover many model
-architectures and a lot of different preprocessing options,
+A lightweight and fast auto-ml library.
 BlueCast focuses on a few model architectures (on default Xgboost
 only) and a few preprocessing options (only what is
 needed for Xgboost). This allows for a much faster development
@@ -486,6 +484,8 @@ class CustomModel(BaseClassMlModel):
     ) -> None:
         self.model = LogisticRegression()
         self.model.fit(x_train, y_train)
+        # if you wih to track experiments using an own ExperimentTracker add it here
+        # or in the fit method itself
 
     def predict(self, df: pd.DataFrame) -> Tuple[PredictedProbas, PredictedClasses]:
         predicted_probas = self.model.predict_proba(df)

bluecast/blueprints/cast.py

@@ -45,19 +45,19 @@ class BlueCast:
     :param :class_problem: Takes a string containing the class problem type. Either "binary" or "multiclass".
     :param :target_column: Takes a string containing the name of the target column.
     :param :cat_columns: Takes a list of strings containing the names of the categorical columns. If not provided,
-    BlueCast will infer these automatically.
+        BlueCast will infer these automatically.
     :param :date_columns: Takes a list of strings containing the names of the date columns. If not provided,
-    BlueCast will infer these automatically.
+        BlueCast will infer these automatically.
     :param :time_split_column: Takes a string containing the name of the time split column. If not provided,
-    BlueCast will not split the data by time or order, but do a random split instead.
+        BlueCast will not split the data by time or order, but do a random split instead.
     :param :ml_model: Takes an instance of a XgboostModel class. If not provided, BlueCast will instantiate one.
-    This is an API to pass any model class. Inherit the baseclass from ml_modelling.base_model.BaseModel.
+        This is an API to pass any model class. Inherit the baseclass from ml_modelling.base_model.BaseModel.
     :param custom_preprocessor: Takes an instance of a CustomPreprocessing class. Allows users to inject custom
-    preprocessing steps which take place right after the train test spit.
+        preprocessing steps which take place right after the train test spit.
     :param custom_last_mile_computation: Takes an instance of a CustomPreprocessing class. Allows users to inject custom
-    preprocessing steps which take place right before the model training.
+        preprocessing steps which take place right before the model training.
     :param experiment_tracker: Takes an instance of an ExperimentTracker class. If not provided this will be initialized
-    automatically.
+        automatically.
     """
 
     def __init__(

bluecast/blueprints/cast_cv.py

@@ -16,9 +16,11 @@
 
 
 class BlueCastCV:
-    """Wrapper to train and predict multiple blueCast intsances.
+    """Wrapper to train and predict multiple blueCast intstances.
 
-    A custom splitter can be provided."""
+    Check the BlueCast class documentation for additional parameter details.
+    A custom splitter can be provided.
+    """
 
     def __init__(
         self,

bluecast/config/training_config.py

@@ -40,6 +40,7 @@ class TrainingConfig(BaseModel):
         categorical encoding is done via a ML algorithm. If False, the categorical encoding is done via a  target
         encoding in the preprocessing steps. See the ReadMe for more details.
     :param show_detailed_tuning_logs: Whether to show detailed tuning logs. Not used when custom ML model is passed.
+    :param experiment_name: Name of the experiment. Will be logged inside the ExperimentTracker.
     """
 
     global_random_state: int = 10

bluecast/experimentation/tracking.py

@@ -8,6 +8,14 @@
 
 
 class ExperimentTracker(BaseClassExperimentTracker):
+    """
+    Default implementation of ExperimentTracker used in BlueCast
+    and BlueCastCV pipelines. This triggers automatically as long
+    as the default Xgboost model is used. For custom ml models
+    ueers need to create an own Tracker. The base class from
+    bluecast.config.base_classes can be used as an inspiration.
+    """
+
     def __init__(self):
         self.experiment_id: List[Union[int, str, float]] = []
         self.experiment_name: List[Union[int, str, float]] = []
@@ -33,6 +41,19 @@ def add_results(
         metric_used: str,
         metric_higher_is_better: bool,
     ) -> None:
+        """
+        Add an individual experiment result into the tracker.
+
+        :param experiment_id: Sequential id. Make sure add an increment.
+        :param score_category: Chose one of ["simple_train_test_score", "cv_score", "oof_score"].
+            "simple_train_test_score" is the default where a simple train-test split is done. "cv_score" is called
+            when cross validation has been enabled in the TrainingConfig.
+        :param training_config: TrainingConfig instance from bluecast.config.training_config.
+        :param model_parameters: Dictionary with parameters of ml model (i.e. learning rate)
+        :param eval_scores: The actual score of the experiment.
+        :param metric_used: The name of the eval metric.
+        :param metric_higher_is_better: True or False.
+        """
         self.experiment_id.append(experiment_id)
         self.score_category.append(score_category)
         try:
@@ -46,6 +67,12 @@ def add_results(
         self.created_at.append(datetime.utcnow())
 
     def retrieve_results_as_df(self) -> pd.DataFrame:
+        """
+        Convert ExperimentTracker information into a Pandas DataFrame.
+
+        In the default implementation this contains TrainingConfig, XgboostConfig, hyperparameters, eval metric
+        and score.
+        """
         model_parameters_df = pd.DataFrame(self.model_parameters)
         training_df = pd.DataFrame(self.training_configs)