autoprognosis.plugins.prediction.risk_estimation.plugin_loglogistic_aft module
- class LogLogisticAFTPlugin(alpha: float = 0.05, l1_ratio: float = 0, model: Optional[Any] = None, random_state: int = 0, **kwargs: Any)
Bases:
autoprognosis.plugins.prediction.risk_estimation.base.RiskEstimationPlugin
Log-Logistic AFT plugin for survival analysis.
- Parameters
alpha – float the level in the confidence intervals.
l1_ratio – float the penalizer coefficient to the size of the coefficients.
random_state – int Random seed
Example
>>> from autoprognosis.plugins.prediction import Predictions >>> from pycox.datasets import metabric >>> >>> df = metabric.read_df() >>> X = df.drop(["duration", "event"], axis=1) >>> Y = df["event"] >>> T = df["duration"] >>> >>> plugin = Predictions(category="risk_estimation").get("loglogistic_aft") >>> plugin.fit(X, T, Y) >>> >>> eval_time_horizons = [int(T[Y.iloc[:] == 1].quantile(0.50))] >>> plugin.predict(X, eval_time_horizons)
- 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.risk_estimation.base.RiskEstimationPlugin
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.risk_estimation.plugin_loglogistic_aft.LogLogisticAFTPlugin
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