gui.core.dimensionalityReductionMethods package

Submodules

gui.core.dimensionalityReductionMethods.dimred_FastICA module

class gui.core.dimensionalityReductionMethods.dimred_FastICA.Ui_Form(n_components=None, *, algorithm='parallel', whiten='unit-variance', fun='logcosh', fun_args=None, max_iter=200, tol=0.0001, w_init=None, whiten_solver='svd', random_state=None)

Bases: Ui_Form, FastICA, Modules

connectWidgets()

Connect the necessary widgets.

Returns:

get_widget()

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

Returns:

run()

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)

set_inverse_transform_request(*, copy: bool | None | str = '$UNCHANGED$') → Ui_Form

Request metadata passed to the inverse_transform 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 inverse_transform 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 inverse_transform.

• 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

copystr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for copy parameter in inverse_transform.

Returns

selfobject

The updated object.

set_transform_request(*, copy: bool | None | str = '$UNCHANGED$') → Ui_Form

Request metadata passed to the transform 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 transform 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 transform.

• 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

copystr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for copy parameter in transform.

Returns

selfobject

The updated object.

setupUi(Form)

gui.core.dimensionalityReductionMethods.dimred_JADE module

class gui.core.dimensionalityReductionMethods.dimred_JADE.Ui_Form

Bases: Ui_Form, Modules

connectWidgets()

Connect the necessary widgets.

Returns:

get_widget()

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

Returns:

run()

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)

setupUi(Form)

gui.core.dimensionalityReductionMethods.dimred_LDA module

class gui.core.dimensionalityReductionMethods.dimred_LDA.Ui_Form(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001, covariance_estimator=None)

Bases: Ui_Form, LinearDiscriminantAnalysis, Modules

connectWidgets()

Connect the necessary widgets.

Returns:

get_widget()

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

Returns:

run()

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)

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

Request metadata passed to the score 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 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.

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 score.

Returns

selfobject

The updated object.

setupUi(Form)

update(new_y_choices)

gui.core.dimensionalityReductionMethods.dimred_LFDA module

class gui.core.dimensionalityReductionMethods.dimred_LFDA.Ui_Form(r=None, metric='plain', knn=5)

Bases: Ui_Form, LFDA, Modules

connectWidgets()

Connect the necessary widgets.

Returns:

get_widget()

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

Returns:

run()

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)

setupUi(Form)

update(new_y_choices)

gui.core.dimensionalityReductionMethods.dimred_LLE module

class gui.core.dimensionalityReductionMethods.dimred_LLE.Ui_Form(*, n_neighbors=5, n_components=2, reg=0.001, eigen_solver='auto', tol=1e-06, max_iter=100, method='standard', hessian_tol=0.0001, modified_tol=1e-12, neighbors_algorithm='auto', random_state=None, n_jobs=None)

Bases: Ui_Form, LocallyLinearEmbedding, Modules

connectWidgets()

Connect the necessary widgets.

Returns:

get_widget()

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

Returns:

run()

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)

setupUi(Form)

gui.core.dimensionalityReductionMethods.dimred_MNF module

class gui.core.dimensionalityReductionMethods.dimred_MNF.Ui_Form

Bases: Ui_Form, MNF, Modules

connectWidgets()

Connect the necessary widgets.

Returns:

get_widget()

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

Returns:

run()

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)

setupUi(Form)

gui.core.dimensionalityReductionMethods.dimred_NNMF module

class gui.core.dimensionalityReductionMethods.dimred_NNMF.Ui_Form(n_components=None, *, init=None, solver='cd', beta_loss='frobenius', tol=0.0001, max_iter=200, random_state=None, alpha_W=0.0, alpha_H='same', l1_ratio=0.0, verbose=0, shuffle=False)

Bases: Ui_Form, NMF, Modules

connectWidgets()

Connect the necessary widgets.

Returns:

get_widget()

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

Returns:

run()

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)

setupUi(Form)

gui.core.dimensionalityReductionMethods.dimred_PCA module

class gui.core.dimensionalityReductionMethods.dimred_PCA.Ui_Form(n_components=None, *, copy=True, whiten=False, svd_solver='auto', tol=0.0, iterated_power='auto', n_oversamples=10, power_iteration_normalizer='auto', random_state=None)

Bases: Ui_Form, PCA, Modules

connectWidgets()

Connect the necessary widgets.

Returns:

get_widget()

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

Returns:

run()

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)

setupUi(Form)

gui.core.dimensionalityReductionMethods.dimred_tSNE module

class gui.core.dimensionalityReductionMethods.dimred_tSNE.Ui_Form(n_components=2, *, perplexity=30.0, early_exaggeration=12.0, learning_rate='auto', n_iter=1000, n_iter_without_progress=300, min_grad_norm=1e-07, metric='euclidean', metric_params=None, init='pca', verbose=0, random_state=None, method='barnes_hut', angle=0.5, n_jobs=None)

Bases: Ui_Form, TSNE, Modules

connectWidgets()

Connect the necessary widgets.

Returns:

get_widget()

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

Returns:

run()

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)

setupUi(Form)

Module contents