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

plugin

alias of autoprognosis.plugins.prediction.risk_estimation.plugin_coxnet.CoxnetRiskEstimationPlugin