autoprognosis.plugins.prediction.risk_estimation.plugin_cox_ph module

class CoxPHPlugin(alpha: float = 0.05, penalizer: float = 0.01, model: Any | None = None, random_state: int = 0, **kwargs: Any)

Bases: RiskEstimationPlugin

CoxPH plugin for survival analysis

Parameters:

• alpha – float the level in the confidence intervals.

• penalizer – float Attach a penalty to the size of the coefficients during regression. This improves stability of the estimates and controls for high correlation between covariates.

• 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("cox_ph")
>>> 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: DataFrame, *args: Any, **kwargs: Any) → DataFrame

fit(X: DataFrame, *args: Any, **kwargs: Any) → RiskEstimationPlugin

Train the plugin

Parameters:

X – pd.DataFrame

fit_predict(X: DataFrame, *args: Any, **kwargs: Any) → DataFrame

Fit the model and predict the training data. Used by predictors.

fit_transform(X: DataFrame, *args: Any, **kwargs: Any) → 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[Params]

The hyperparameter search domain, used for tuning.

classmethod hyperparameter_space_fqdn(*args: Any, **kwargs: Any) → List[Params]

The hyperparameter domain using they fully-qualified name.

is_fitted() → bool

Check if the model was trained

classmethod load(buff: bytes) → CoxPHPlugin

Load the plugin from bytes

static name() → str

The name of the plugin, e.g.: xgboost

predict(X: DataFrame, *args: Any, **kwargs: Any) → DataFrame

Run predictions for the input. Used by predictors.

Parameters:

X – pd.DataFrame

predict_proba(X: DataFrame, *args: Any, **kwargs: Any) → 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: DataFrame, y: DataFrame, metric: str = 'aucroc') → float

static subtype() → str

The type of the plugin, e.g.: classifier

transform(X: DataFrame) → 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 CoxPHPlugin