autoprognosis.plugins.prediction.regression.plugin_xgboost_regressor module
- class XGBoostRegressorPlugin(reg_lambda: Optional[float] = None, reg_alpha: Optional[float] = None, colsample_bytree: Optional[float] = None, colsample_bynode: Optional[float] = None, colsample_bylevel: Optional[float] = None, n_estimators: int = 100, max_depth: Optional[int] = 3, lr: Optional[float] = None, subsample: Optional[float] = None, min_child_weight: Optional[int] = None, max_bin: int = 256, booster: int = 0, grow_policy: int = 0, eta: float = 0.3, model: Optional[Any] = None, random_state: int = 0, hyperparam_search_iterations: Optional[int] = None, **kwargs: Any)
Bases:
autoprognosis.plugins.prediction.regression.base.RegressionPlugin
Regression plugin based on the XGBoost.
- Method:
Gradient boosting is a supervised learning algorithm that attempts to accurately predict a target variable by combining an ensemble of estimates from a set of simpler and weaker models. The XGBoostRegressor algorithm has a robust handling of a variety of data types, relationships, distributions, and the variety of hyperparameters that you can fine-tune.
- Parameters
n_estimators – int The maximum number of estimators at which boosting is terminated.
max_depth – int Maximum depth of a tree.
reg_lambda – float L2 regularization term on weights (xgb’s lambda).
reg_alpha – float L1 regularization term on weights (xgb’s alpha).
colsample_bytree – float Subsample ratio of columns when constructing each tree.
colsample_bynode – float Subsample ratio of columns for each split.
colsample_bylevel – float Subsample ratio of columns for each level.
subsample – float Subsample ratio of the training instance.
learning_rate – float Boosting learning rate
booster – str Specify which booster to use: gbtree, gblinear or dart.
min_child_weight – int Minimum sum of instance weight(hessian) needed in a child.
max_bin – int Number of bins for histogram construction.
random_state – float Random number seed.
Example
>>> from autoprognosis.plugins.prediction import Predictions >>> plugin = Predictions(category="regressors").get("xgboost_regressor") >>> from sklearn.datasets import load_iris >>> X, y = load_iris(return_X_y=True) >>> plugin.fit_predict(X, y)
- booster = ['gbtree', 'gblinear', 'dart']
- change_output(output: str) None
- explain(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) pandas.core.frame.DataFrame
- fit(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) autoprognosis.plugins.prediction.regression.base.RegressionPlugin
Train the plugin
- Parameters
X – pd.DataFrame
- fit_predict(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) pandas.core.frame.DataFrame
Fit the model and predict the training data. Used by predictors.
- fit_transform(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) pandas.core.frame.DataFrame
Fit the model and transform the training data. Used by imputers and preprocessors.
- classmethod fqdn() str
The fully-qualified name of the plugin: type->subtype->name
- get_args() dict
- grow_policy = ['depthwise', 'lossguide']
- static hyperparameter_space(*args: Any, **kwargs: Any) List[autoprognosis.plugins.core.params.Params]
The hyperparameter search domain, used for tuning.
- classmethod hyperparameter_space_fqdn(*args: Any, **kwargs: Any) List[autoprognosis.plugins.core.params.Params]
The hyperparameter domain using they fully-qualified name.
- is_fitted() bool
Check if the model was trained
- classmethod load(buff: bytes) autoprognosis.plugins.prediction.regression.plugin_xgboost_regressor.XGBoostRegressorPlugin
Load the plugin from bytes
- static name() str
The name of the plugin, e.g.: xgboost
- predict(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) pandas.core.frame.DataFrame
Run predictions for the input. Used by predictors.
- Parameters
X – pd.DataFrame
- predict_proba(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) pandas.core.frame.DataFrame
- classmethod sample_hyperparameters(trial: optuna.trial.Trial, *args: Any, **kwargs: Any) Dict[str, Any]
Sample hyperparameters for Optuna.
- classmethod sample_hyperparameters_fqdn(trial: optuna.trial.Trial, *args: Any, **kwargs: Any) Dict[str, Any]
Sample hyperparameters using they fully-qualified name.
- classmethod sample_hyperparameters_np(random_state: int = 0, *args: Any, **kwargs: Any) Dict[str, Any]
Sample hyperparameters as a dict.
- save() bytes
Save the plugin to bytes
- score(X: pandas.core.frame.DataFrame, y: pandas.core.frame.DataFrame, metric: str = 'aucroc') float
- static subtype() str
The type of the plugin, e.g.: classifier
- transform(X: pandas.core.frame.DataFrame) pandas.core.frame.DataFrame
Transform the input. Used by imputers and preprocessors.
- Parameters
X – pd.DataFrame
- static type() str
The type of the plugin, e.g.: prediction