autoprognosis.plugins.prediction.classifiers.plugin_lgbm module

class LightGBMPlugin(n_estimators: int = 100, boosting_type: str = 'gbdt', learning_rate: float = 0.01, max_depth: int = 6, reg_lambda: float = 0.001, reg_alpha: float = 0.001, colsample_bytree: float = 0.1, subsample: float = 0.1, num_leaves: int = 31, min_child_samples: int = 1, calibration: int = 0, model: Optional[Any] = None, random_state: int = 0, **kwargs: Any)

Bases: autoprognosis.plugins.prediction.classifiers.base.ClassifierPlugin

Classification plugin based on LightGBM.

Method:

Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. When a decision tree is the weak learner, the resulting algorithm is called gradient boosted trees, which usually outperforms random forest.

Parameters

  • n_estimators – int The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large number usually results in better performance.

  • learning_rate – float Learning rate shrinks the contribution of each tree by learning_rate. There is a trade-off between learning_rate and n_estimators.

  • max_depth – int The maximum depth of the individual regression estimators.

  • boosting_type – str ‘gbdt’, traditional Gradient Boosting Decision Tree. ‘dart’, Dropouts meet Multiple Additive Regression Trees. ‘goss’, Gradient-based One-Side Sampling. ‘rf’, Random Forest.

  • objective – str Specify the learning task and the corresponding learning objective or a custom objective function to be used.

  • reg_lambda – float L2 regularization term on weights.

  • reg_alpha – float L1 regularization term on weights.

  • colsample_bytree – float Subsample ratio of columns when constructing each tree.

  • subsample – float Subsample ratio of the training instance.

  • num_leaves – int Maximum tree leaves for base learners.

  • min_child_samples – int Minimum sum of instance weight (hessian) needed in a child (leaf).

  • calibration – int Enable/disable calibration. 0: disabled, 1 : sigmoid, 2: isotonic.

  • random_state – int, default 0 Random seed

Example

>>> from autoprognosis.plugins.prediction import Predictions
>>> plugin = Predictions(category="classifiers").get("lgbm")
>>> 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.core.base_plugin.Plugin

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
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.classifiers.plugin_lgbm.LightGBMPlugin

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.classifiers.plugin_lgbm.LightGBMPlugin