autoprognosis.plugins.prediction.risk_estimation.plugin_coxnet module
- class CoxnetRiskEstimationPlugin(hidden_dim: int = 100, hidden_len: int = 2, batch_norm: bool = True, dropout: float = 0.1, lr: float = 0.001, epochs: int = 5000, patience: int = 50, batch_size: int = 128, verbose: bool = False, random_state: int = 0, **kwargs: Any)
Bases:
autoprognosis.plugins.prediction.risk_estimation.base.RiskEstimationPlugin
CoxPH neural net plugin for survival analysis.
- Parameters
hidden_dim – int Number of neurons in the hidden layers
hidden_len – int Number of hidden layers
batch_norm – bool. Batch norm on/off.
dropout – float. Dropout value.
lr – float. Learning rate.
epochs – int. Number of training epochs
patience – int. Number of iterations without validation improvement.
batch_size – int. Batch size
verbose – bool. Enable debug logs
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("coxnet") >>> 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]
Return the hyperparameter space for the current model.
- 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_coxnet.CoxnetRiskEstimationPlugin
Load the plugin from bytes
- static name() str
Return the name of the current model.
- 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