Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the What you really need to know section for the big picture.

julearn.base.WrapModel

class julearn.base.WrapModel(model, apply_to=None, needed_types=None, **params)

Wrap a model to make it a julearn estimator.

Parameters:

  • model (ModelLike) – The model to wrap.

  • apply_to (list[str] | set[str] | str | ColumnTypes | None, default: None) – The column types to apply the model to. If None, the model is applied to continuous type (default is None).

  • needed_types (list[str] | set[str] | str | ColumnTypes | None, default: None) – The column types needed by the model. If None, there are no needed types (default is None)

  • **params – The parameters to set on the model.

__init__(model, apply_to=None, needed_types=None, **params)
fit(X, y=None, **fit_params)

Fit the model.

This method will fit the model using only the columns selected by apply_to.

Parameters:
Returns:

The fitted model.

predict(X)

Predict using the model.

Parameters:

X (DataFrame) – The data to predict on.

Returns:

The predictions.

score(X, y)

Score the model.

Parameters:
Returns:

The score.

predict_proba(X)

Compute probabilities of possible outcomes for samples in X.

Parameters:

X (DataFrame) – The data to predict on.

Returns:

Returns the probability of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

decision_function(X)

Evaluate the decision function for the samples in X.

Parameters:

X (DataFrame) – The data to obtain the decision function.

Returns:

Returns the decision function of the sample for each class in the model. Shape is (n_samples, n_class * (n_class-1) / 2).

predict_log_proba(X)

Compute probabilities of possible outcomes for samples in X.

Parameters:

X (DataFrame) – The data to predict on.

Returns:

Returns the probability of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

property classes_: ndarray

Get the classes of the model.

get_params(deep=True)

Get the parameters of the model.

Parameters:

deep (bool, default: True) – If True, will return the parameters for this model and contained subobjects that are estimators (default is True).

Returns:

Parameter names mapped to their values.

set_params(**kwargs)

Set the parameters of this model.

The method works on simple models as well as on nested objects (such as sklearn.pipeline.Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**kwargs (Any) – Model parameters.

Returns:

WrapModel instance.

filter_columns(X)

Get the apply_to columns of a pandas DataFrame.

Parameters:

X (DataFrame) – The DataFrame to filter.

Returns:

The DataFrame with only the apply_to columns.

get_apply_to()

Get the column types the estimator applies to.

Returns:

The column types the estimator applies to.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

A MetadataRequest encapsulating routing information.

get_needed_types()

Get the column types needed by the estimator.

Returns:

The column types needed by the estimator.