onetick.ml.utils
onetick.ml.utils#
- build_experiment(config: Union[dict, str] = {}, globals_dict: dict = {})[source]#
Build onetick.ml.Experiment instance from dict-config
- Parameters
config (dict) – Configuration dict with settings tree
globals_dict (dict, optional) – Supply globals() dict, to restore custom classes. Defaults to {}.
- Returns
onetick.ml.Experiment class instance builded from config dict
- Return type
any
- build_experiment_class(config: Union[dict, str] = {}, globals_dict: dict = {})[source]#
Build onetick.ml.Experiment class from dict-config
- Parameters
config (dict) – Configuration dict with settings tree
globals_dict (dict, optional) – Supply globals() dict, to restore custom classes. Defaults to {}.
- Returns
onetick.ml.Experiment class builded from config dict
- Return type
any
- restore_experiment_from_mlflow(run_id: str, mlflow_url: Optional[str] = None)[source]#
Builds Experiment class from YAML config loaded from MLFlow.
- Parameters
run_id (str) – MLFlow Run ID to restore from
mlflow_url (Optional[str], optional) – MLFlow Tracking URI. Defaults to None.
- Returns
onetick.ml.Experiment
- Return type
instance inherited from onetick.ml.Experiment class restored from MLFlow run
- params_iterator(model_params)[source]#
Iterate over grid returning combinations.
- Parameters
model_params (dict) – Grid of parameters to iterate over
- Yields
dict – One combination of parameters from grid.
- create_folds(x_train=None, x_val=None, y_val=None, val_type: Literal['None', 'Simple', 'Cross', 'WalkForward'] = 'None', folds_num=5)[source]#
Create folds for cross-validation
- Parameters
x_train (pandas.DataFrame, optional) – Features for training, by default None
x_val (pandas.DataFrame, optional) – Features for validation, by default None
y_val (pandas.DataFrame, optional) – Targets for validation, by default None
val_type (Literal["None", "Simple", "Cross", "WalkForward"], optional) – Cross-validation type, by default “None”
folds_num (int, optional) – Number of folds, by default 5
- Returns
Folds indices generator used for cross-validation (GridSearchCV or RandomizedSearchCV) in onetick.ml.Experiment
- Return type
any
- class ExperimentBuilder(config: Union[dict, str] = {}, globals_dict: dict = {})[source]#
Builder class used to restore Experiment from YAML/dict config.
- Parameters
config (Union[dict, str]) – YAML config or dict with config.
globals_dict (dict) – Dict with globals, used to restore classes from global scope.
- static get_class_name(class_obj)[source]#
Returns class name with module name.
- Parameters
class_obj (class) – Class object.
- Returns
Class name with module name.
- Return type
str