autoprognosis.plugins.prediction.regression.plugin_catboost_regressor module

class CatBoostRegressorPlugin(depth: int = 5, grow_policy: int = 0, n_estimators: int = 100, l2_leaf_reg: float = 3, learning_rate: float = 0.001, min_data_in_leaf: int = 1, random_strength: float = 1, model: Optional[Any] = None, hyperparam_search_iterations: Optional[int] = None, random_state: int = 0, **kwargs: Any)

Bases: autoprognosis.plugins.prediction.regression.base.RegressionPlugin

Regression plugin based on the CatBoost framework.

Method:

CatBoost provides a gradient boosting framework which attempts to solve for Categorical features using a permutation driven alternative compared to the classical algorithm. It uses Ordered Boosting to overcome over fitting and Symmetric Trees for faster execution.

Parameters

• n_estimators – int Number of gradient boosted trees. Equivalent to number of boosting rounds.

• depth – int Depth of the tree.

• grow_policy – int The tree growing policy. Defines how to perform greedy tree construction: [SymmetricTree, Depthwise]

• l2_leaf_reg – float Coefficient at the L2 regularization term of the cost function.

• learning_rate – float The learning rate used for reducing the gradient step.

• min_data_in_leaf – int The minimum number of training samples in a leaf.

• random_strength – float The amount of randomness to use for scoring splits when the tree structure is selected. Use this parameter to avoid overfitting the model.

• random_state – int, default 0 Random seed

Example

>>> from autoprognosis.plugins.prediction import Predictions
>>> plugin = Predictions(category="regression").get("catboost_regressor")
>>> from sklearn.datasets import load_iris
>>> X, y = load_iris(return_X_y=True)
>>> plugin.fit_predict(X, y) # returns the probabilities for each class

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_policies = ['Depthwise', 'SymmetricTree', '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_catboost_regressor.CatBoostRegressorPlugin

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

plugin

alias of autoprognosis.plugins.prediction.regression.plugin_catboost_regressor.CatBoostRegressorPlugin