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:

X (ndarray | DataFrame | Series) – The data to fit the model on.
y (ndarray | DataFrame | Series | None, default: None) – The target data (default is None).
**fit_params (Any) – Additional parameters to pass to the model’s fit method.

Returns:

The fitted model.

predict(X)¶

Predict using the model.

Parameters:: X (DataFrame) – The data to predict on.
Returns:: The predictions.

score(X, y, sample_weight=None)¶

Score the model.

Parameters:

X (DataFrame) – The data to predict on.
y (ndarray | DataFrame | Series) – The true target values.
sample_weight (ArrayLike | None, default: None) – Sample weights to pass to the model’s score method (default None).

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.
Raises:: ValueError – If a parameter is not a parameter of this model or of the wrapped model.

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.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → WrapModel¶

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:: sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.
Returns:: The updated object.