Merge pull request #3543 from pycaret/remove_leaveoneout

Remove leaveoneout

pycaret/anomaly/functional.py

@@ -78,7 +78,6 @@ def setup(
     profile: bool = False,
     profile_kwargs: Optional[Dict[str, Any]] = None,
 ):
-
     """
     This function initializes the training environment and creates the transformation
     pipeline. Setup function must be called before executing any other function. It
@@ -516,7 +515,6 @@ def create_model(
     experiment_custom_tags: Optional[Dict[str, Any]] = None,
     **kwargs,
 ):
-
     """
     This function trains a given model from the model library. All available
     models can be accessed using the ``models`` function.
@@ -592,7 +590,6 @@ def create_model(
 def assign_model(
     model, transformation: bool = False, score: bool = True, verbose: bool = True
 ) -> pd.DataFrame:
-
     """
     This function assigns anomaly labels to the dataset for a given model.
     (1 = outlier, 0 = inlier).
@@ -643,7 +640,6 @@ def plot_model(
     save: bool = False,
     display_format: Optional[str] = None,
 ) -> Optional[str]:
-
     """
     This function analyzes the performance of a trained model.
 
@@ -713,7 +709,6 @@ def evaluate_model(
     feature: Optional[str] = None,
     fit_kwargs: Optional[dict] = None,
 ):
-
     """
     This function displays a user interface for analyzing performance of a trained
     model. It calls the ``plot_model`` function internally.
@@ -759,7 +754,6 @@ def evaluate_model(
 
 # not using check_if_global_is_not_none on purpose
 def predict_model(model, data: pd.DataFrame) -> pd.DataFrame:
-
     """
     This function generates anomaly labels on using a trained model.
 
@@ -811,7 +805,6 @@ def deploy_model(
     authentication: dict,
     platform: str = "aws",
 ):
-
     """
     This function deploys the transformation pipeline and trained model on cloud.
 
@@ -903,7 +896,6 @@ def deploy_model(
 def save_model(
     model, model_name: str, model_only: bool = False, verbose: bool = True, **kwargs
 ):
-
     """
     This function saves the transformation pipeline and trained model object
     into the current working directory as a pickle file for later use.
@@ -961,7 +953,6 @@ def load_model(
     authentication: Optional[Dict[str, str]] = None,
     verbose: bool = True,
 ):
-
     """
     This function loads a previously saved pipeline.
 
@@ -1039,7 +1030,6 @@ def models(
     internal: bool = False,
     raise_errors: bool = True,
 ) -> pd.DataFrame:
-
     """
     Returns table of models available in the model library.
 
@@ -1071,7 +1061,6 @@ def models(
 
 @check_if_global_is_not_none(globals(), _CURRENT_EXPERIMENT_DECORATOR_DICT)
 def get_logs(experiment_name: Optional[str] = None, save: bool = False) -> pd.DataFrame:
-
     """
     Returns a table of experiment logs. Only works when ``log_experiment``
     is True when initializing the ``setup`` function.
@@ -1105,7 +1094,6 @@ def get_logs(experiment_name: Optional[str] = None, save: bool = False) -> pd.Da
 
 @check_if_global_is_not_none(globals(), _CURRENT_EXPERIMENT_DECORATOR_DICT)
 def get_config(variable: Optional[str] = None):
-
     """
     This function is used to access global environment variables.
 
@@ -1132,7 +1120,6 @@ def get_config(variable: Optional[str] = None):
 
 @check_if_global_is_not_none(globals(), _CURRENT_EXPERIMENT_DECORATOR_DICT)
 def set_config(variable: str, value):
-
     """
     This function is used to reset global environment variables.
 
@@ -1184,7 +1171,6 @@ def load_experiment(
     preprocess_data: bool = True,
     **cloudpickle_kwargs,
 ) -> AnomalyExperiment:
-
     """
     Load an experiment saved with ``save_experiment`` from path
     or file.

pycaret/classification/functional.py

@@ -97,7 +97,6 @@ def setup(
     profile: bool = False,
     profile_kwargs: Optional[Dict[str, Any]] = None,
 ):
-
     """
     This function initializes the training environment and creates the transformation
     pipeline. Setup function must be called before executing any other function. It takes
@@ -266,7 +265,7 @@ def setup(
     encoding_method: category-encoders estimator, default = None
         A `category-encoders` estimator to encode the categorical columns
         with more than `max_encoding_ohe` unique values. If None,
-        `category_encoders.leave_one_out.LeaveOneOutEncoder` is used.
+        `category_encoders.target_encoder.TargetEncoder` is used.
 
 
     rare_to_value: float or None, default=None
@@ -698,7 +697,6 @@ def compare_models(
     verbose: bool = True,
     parallel: Optional[ParallelBackend] = None,
 ) -> Union[Any, List[Any]]:
-
     """
     This function trains and evaluates performance of all estimators available in the
     model library using cross validation. The output of this function is a score grid
@@ -894,7 +892,6 @@ def create_model(
     return_train_score: bool = False,
     **kwargs,
 ) -> Any:
-
     """
     This function trains and evaluates the performance of a given estimator
     using cross validation. The output of this function is a score grid with
@@ -1047,7 +1044,6 @@ def tune_model(
     return_train_score: bool = False,
     **kwargs,
 ) -> Any:
-
     """
     This function tunes the hyperparameters of a given estimator. The output of
     this function is a score grid with CV scores by fold of the best selected
@@ -1255,7 +1251,6 @@ def ensemble_model(
     verbose: bool = True,
     return_train_score: bool = False,
 ) -> Any:
-
     """
     This function ensembles a given estimator. The output of this function is
     a score grid with CV scores by fold. Metrics evaluated during CV can be
@@ -1376,7 +1371,6 @@ def blend_models(
     verbose: bool = True,
     return_train_score: bool = False,
 ) -> Any:
-
     """
     This function trains a Soft Voting / Majority Rule classifier for select
     models passed in the ``estimator_list`` param. The output of this function
@@ -1499,7 +1493,6 @@ def stack_models(
     verbose: bool = True,
     return_train_score: bool = False,
 ) -> Any:
-
     """
     This function trains a meta model over select estimators passed in
     the ``estimator_list`` parameter. The output of this function is a
@@ -1637,7 +1630,6 @@ def plot_model(
     verbose: bool = True,
     display_format: Optional[str] = None,
 ) -> Optional[str]:
-
     """
     This function analyzes the performance of a trained model on holdout set.
     It may require re-training the model in certain cases.
@@ -1768,7 +1760,6 @@ def evaluate_model(
     groups: Optional[Union[str, Any]] = None,
     use_train_data: bool = False,
 ):
-
     """
     This function displays a user interface for analyzing performance of a trained
     model. It calls the ``plot_model`` function internally.
@@ -1847,7 +1838,6 @@ def interpret_model(
     save: Union[str, bool] = False,
     **kwargs,
 ):
-
     """
     This function takes a trained model object and returns an interpretation plot
     based on the test / hold-out set.
@@ -1959,7 +1949,6 @@ def calibrate_model(
     verbose: bool = True,
     return_train_score: bool = False,
 ) -> Any:
-
     """
     This function calibrates the probability of a given estimator using isotonic
     or logistic regression. The output of this function is a score grid with CV
@@ -2062,7 +2051,6 @@ def optimize_threshold(
     plot_kwargs: Optional[dict] = None,
     **shgo_kwargs,
 ):
-
     """
     This function optimizes probability threshold for a trained classifier. It
     uses the SHGO optimizer from ``scipy`` to optimize for the given metric.
@@ -2131,7 +2119,6 @@ def predict_model(
     round: int = 4,
     verbose: bool = True,
 ) -> pd.DataFrame:
-
     """
     This function predicts ``Label`` and ``Score`` (probability of predicted
     class) using a trained model. When ``data`` is None, it predicts label and
@@ -2217,7 +2204,6 @@ def finalize_model(
     model_only: bool = False,
     experiment_custom_tags: Optional[Dict[str, Any]] = None,
 ) -> Any:
-
     """
     This function trains a given estimator on the entire dataset including the
     holdout set.
@@ -2368,7 +2354,6 @@ def deploy_model(
 def save_model(
     model, model_name: str, model_only: bool = False, verbose: bool = True, **kwargs
 ):
-
     """
     This function saves the transformation pipeline and trained model object
     into the current working directory as a pickle file for later use.
@@ -2425,7 +2410,6 @@ def load_model(
     authentication: Optional[Dict[str, str]] = None,
     verbose: bool = True,
 ):
-
     """
     This function loads a previously saved pipeline.
 
@@ -2486,7 +2470,6 @@ def automl(
     turbo: bool = True,
     return_train_score: bool = False,
 ) -> Any:
-
     """
     This function returns the best model out of all trained models in
     current session based on the ``optimize`` parameter. Metrics
@@ -2566,7 +2549,6 @@ def models(
     internal: bool = False,
     raise_errors: bool = True,
 ) -> pd.DataFrame:
-
     """
     Returns table of models available in the model library.
 
@@ -2609,7 +2591,6 @@ def get_metrics(
     include_custom: bool = True,
     raise_errors: bool = True,
 ) -> pd.DataFrame:
-
     """
     Returns table of available metrics used in the experiment.
 
@@ -2659,7 +2640,6 @@ def add_metric(
     multiclass: bool = True,
     **kwargs,
 ) -> pd.Series:
-
     """
     Adds a custom metric to be used in the experiment.
 
@@ -2724,7 +2704,6 @@ def add_metric(
 
 @check_if_global_is_not_none(globals(), _CURRENT_EXPERIMENT_DECORATOR_DICT)
 def remove_metric(name_or_id: str):
-
     """
     Removes a metric from the experiment.
 
@@ -2751,7 +2730,6 @@ def remove_metric(name_or_id: str):
 
 @check_if_global_is_not_none(globals(), _CURRENT_EXPERIMENT_DECORATOR_DICT)
 def get_logs(experiment_name: Optional[str] = None, save: bool = False) -> pd.DataFrame:
-
     """
     Returns a table of experiment logs. Only works when ``log_experiment``
     is True when initializing the ``setup`` function.
@@ -2785,7 +2763,6 @@ def get_logs(experiment_name: Optional[str] = None, save: bool = False) -> pd.Da
 
 @check_if_global_is_not_none(globals(), _CURRENT_EXPERIMENT_DECORATOR_DICT)
 def get_config(variable: Optional[str] = None):
-
     """
     This function is used to access global environment variables.
 
@@ -2812,7 +2789,6 @@ def get_config(variable: Optional[str] = None):
 
 @check_if_global_is_not_none(globals(), _CURRENT_EXPERIMENT_DECORATOR_DICT)
 def set_config(variable: str, value):
-
     """
     This function is used to reset global environment variables.
 
@@ -2866,7 +2842,6 @@ def load_experiment(
     preprocess_data: bool = True,
     **cloudpickle_kwargs,
 ) -> ClassificationExperiment:
-
     """
     Load an experiment saved with ``save_experiment`` from path
     or file.
@@ -2938,7 +2913,6 @@ def get_leaderboard(
     groups: Optional[Union[str, Any]] = None,
     verbose: bool = True,
 ) -> pd.DataFrame:
-
     """
     This function returns the leaderboard of all models trained in the
     current setup.
@@ -3049,7 +3023,6 @@ def dashboard(
 
 @check_if_global_is_not_none(globals(), _CURRENT_EXPERIMENT_DECORATOR_DICT)
 def convert_model(estimator, language: str = "python") -> str:
-
     """
     This function transpiles trained machine learning models into native
     inference script in different programming languages (Python, C, Java,
@@ -3102,7 +3075,6 @@ def convert_model(estimator, language: str = "python") -> str:
 
 @check_if_global_is_not_none(globals(), _CURRENT_EXPERIMENT_DECORATOR_DICT)
 def eda(display_format: str = "bokeh", **kwargs):
-
     """
     This function generates AutoEDA using AutoVIZ library. You must
     install Autoviz separately ``pip install autoviz`` to use this
@@ -3134,7 +3106,6 @@ def eda(display_format: str = "bokeh", **kwargs):
 
 @check_if_global_is_not_none(globals(), _CURRENT_EXPERIMENT_DECORATOR_DICT)
 def check_fairness(estimator, sensitive_features: list, plot_kwargs: dict = {}):
-
     """
     There are many approaches to conceptualizing fairness. This function follows
     the approach known as group fairness, which asks: Which groups of individuals
@@ -3180,7 +3151,6 @@ def check_fairness(estimator, sensitive_features: list, plot_kwargs: dict = {}):
 def create_api(
     estimator, api_name: str, host: str = "127.0.0.1", port: int = 8000
 ) -> None:
-
     """
     This function takes an input ``estimator`` and creates a POST API for
     inference. It only creates the API and doesn't run it automatically.
@@ -3226,7 +3196,6 @@ def create_api(
 def create_docker(
     api_name: str, base_image: str = "python:3.8-slim", expose_port: int = 8000
 ) -> None:
-
     """
     This function creates a ``Dockerfile`` and ``requirements.txt`` for
     productionalizing API end-point.
@@ -3265,7 +3234,6 @@ def create_docker(
 
 @check_if_global_is_not_none(globals(), _CURRENT_EXPERIMENT_DECORATOR_DICT)
 def create_app(estimator, app_kwargs: Optional[dict] = None) -> None:
-
     """
     This function creates a basic gradio app for inference.
     It will later be expanded for other app types such as