gui.core.outlierMethods package

Submodules

gui.core.outlierMethods.outliers_IsolationForest module

class gui.core.outlierMethods.outliers_IsolationForest.Ui_Form(*, n_estimators=100, max_samples='auto', contamination='auto', max_features=1.0, bootstrap=False, n_jobs=None, random_state=None, verbose=0, warm_start=False)[source]

Bases: Ui_Form, IsolationForest, Modules

connectWidgets()[source]

Connect the necessary widgets.

Returns:

get_widget()[source]

This function specifies the variable that holds the styling. Use this function to get the variable

Returns:

run()[source]: Each Module’s functionality will be ran in this function. You will define what will happen to the data and parameters in here :return:

setHidden(bool)[source]

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → Ui_Form

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit 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 fit.
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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.

Returns

selfobject: The updated object.

setupUi(Form)[source]

gui.core.outlierMethods.outliers_LOF module

class gui.core.outlierMethods.outliers_LOF.Ui_Form(n_neighbors=20, *, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, contamination='auto', novelty=False, n_jobs=None)[source]

Bases: Ui_Form, LocalOutlierFactor, Modules