summit.multiview_platform.monoview_classifiers package

Subpackages

Submodules

summit.multiview_platform.monoview_classifiers.adaboost module

class Adaboost(random_state=None, n_estimators=50, estimator=None, estimator_config=None, **kwargs)

Bases: AdaBoostClassifier, BaseMonoviewClassifier

This class is an adaptation of scikit-learn’s AdaBoostClassifier

fit(X, y, sample_weight=None)

Build a boosted classifier/regressor from the training set (X, y).

Parameters:

  • X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

  • y (array-like of shape (n_samples,)) – The target values.

  • sample_weight (array-like of shape (n_samples,), default=None) – Sample weights. If None, the sample weights are initialized to 1 / n_samples.

Returns:

self – Fitted estimator.

Return type:

object

get_interpretation(directory, base_file_name, y_test, feature_ids, multi_class=False)

Base method that returns an empty string if there is not interpretation method in the classifier’s module

predict(X)

Predict classes for X.

The predicted class of an input sample is computed as the weighted mean prediction of the classifiers in the ensemble.

Parameters:

X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Returns:

y – The predicted classes.

Return type:

ndarray of shape (n_samples,)

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

summit.multiview_platform.monoview_classifiers.decision_tree module

class DecisionTree(random_state=None, max_depth=None, criterion='gini', splitter='best', **kwargs)

Bases: DecisionTreeClassifier, BaseMonoviewClassifier

This class is an adaptation of scikit-learn’s DecisionTreeClassifier

get_interpretation(directory, base_file_name, y_test, feature_ids, multiclass=False)

Base method that returns an empty string if there is not interpretation method in the classifier’s module

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

summit.multiview_platform.monoview_classifiers.gradient_boosting module

class CustomDecisionTreeGB(*, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, class_weight=None, ccp_alpha=0.0, monotonic_cst=None)

Bases: DecisionTreeClassifier

predict(X, check_input=True)

Predict class or regression value for X.

For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned.

Parameters:

  • X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

  • check_input (bool, default=True) – Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing.

Returns:

y – The predicted classes, or the predict values.

Return type:

array-like of shape (n_samples,) or (n_samples, n_outputs)

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

class GradientBoosting(random_state=None, loss='exponential', max_depth=1.0, n_estimators=100, init=CustomDecisionTreeGB(max_depth=1), **kwargs)

Bases: GradientBoostingClassifier, BaseMonoviewClassifier

This class is an adaptation of scikit-learn’s GradientBoostingClassifier

fit(X, y, sample_weight=None, monitor=None)

Fit the gradient boosting model.

Parameters:

  • X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

  • y (array-like of shape (n_samples,)) – Target values (strings or integers in classification, real numbers in regression) For classification, labels must correspond to classes.

  • sample_weight (array-like of shape (n_samples,), default=None) – Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node.

  • monitor (callable, default=None) – The monitor is called after each iteration with the current iteration, a reference to the estimator and the local variables of _fit_stages as keyword arguments callable(i, self, locals()). If the callable returns True the fitting procedure is stopped. The monitor can be used for various things such as computing held-out estimates, early stopping, model introspect, and snapshotting.

Returns:

self – Fitted estimator.

Return type:

object

get_interpretation(directory, base_file_name, y_test, feature_ids, multi_class=False)

Base method that returns an empty string if there is not interpretation method in the classifier’s module

predict(X)

Predict class for X.

Parameters:

X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.

Returns:

y – The predicted values.

Return type:

ndarray of shape (n_samples,)

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

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.

Added 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:

  • monitor (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for monitor parameter in fit.

  • sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

summit.multiview_platform.monoview_classifiers.imbalance_bagging module

class ImbalanceBagging(random_state=None, estimator='DecisionTreeClassifier', n_estimators=10, sampling_strategy='auto', replacement=False, base_estimator_config=None)

Bases: BaseMonoviewClassifier, BalancedBaggingClassifier

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

summit.multiview_platform.monoview_classifiers.knn module

class KNN(random_state=None, n_neighbors=5, weights='uniform', algorithm='auto', p=2, **kwargs)

Bases: KNeighborsClassifier, BaseMonoviewClassifier

This class is an adaptation of scikit-learn’s KNeighborsClassifier

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

summit.multiview_platform.monoview_classifiers.lasso module

class Lasso(random_state=None, alpha=1.0, max_iter=10, warm_start=False, **kwargs)

Bases: Lasso, BaseMonoviewClassifier

This class is an adaptation of scikit-learn’s Lasso

fit(X, y, check_input=True)

Fit model with coordinate descent.

Parameters:

  • X ({ndarray, sparse matrix, sparse array} of (n_samples, n_features)) –

    Data.

    Note that large sparse matrices and arrays requiring int64 indices are not accepted.

  • y (ndarray of shape (n_samples,) or (n_samples, n_targets)) – Target. Will be cast to X’s dtype if necessary.

  • sample_weight (float or array-like of shape (n_samples,), default=None) –

    Sample weights. Internally, the sample_weight vector will be rescaled to sum to n_samples.

    Added in version 0.23.

  • check_input (bool, default=True) – Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

Returns:

self – Fitted estimator.

Return type:

object

Notes

Coordinate descent is an algorithm that considers each column of data at a time hence it will automatically convert the X input as a Fortran-contiguous numpy array if necessary.

To avoid memory re-allocation it is advised to allocate the initial data in memory directly using that format.

predict(X)

Predict using the linear model.

Parameters:

X (array-like or sparse matrix, shape (n_samples, n_features)) – Samples.

Returns:

C – Returns predicted values.

Return type:

array, shape (n_samples,)

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

summit.multiview_platform.monoview_classifiers.random_forest module

class RandomForest(random_state=None, n_estimators=10, max_depth=None, criterion='gini', **kwargs)

Bases: RandomForestClassifier, BaseMonoviewClassifier

This class is an adaptation of scikit-learn’s RandomForestClassifier

get_interpretation(directory, base_file_name, y_test, feature_ids, multiclass=False)

Base method that returns an empty string if there is not interpretation method in the classifier’s module

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

summit.multiview_platform.monoview_classifiers.random_scm module

class ScmBagging(n_estimators=100, max_samples=0.5, max_features=0.5, max_rules=10, p_options=[1.0], model_type='conjunction', random_state=None)

Bases: RandomScmClassifier, BaseMonoviewClassifier

A Bagging classifier. for SetCoveringMachineClassifier() The estimators are built on subsets of both samples and features. :param n_estimators: The number of estimators in the ensemble. :type n_estimators: int, default=10 :param max_samples: The number of samples to draw from X to train each estimator with

replacement. - If int, then draw max_samples samples. - If float, then draw max_samples * X.shape[0] samples.

Parameters:

  • max_features (int or float, default=1.0) – The number of features to draw from X to train each estimator ( without replacement. - If int, then draw max_features features. - If float, then draw max_features * X.shape[1] features.

  • p_options (list of float with len =< n_estimators, default=[1.0]) – The estimators will be fitted with values of p found in p_options let k be k = n_estimators/len(p_options), the k first estimators will have p=p_options[0], the next k estimators will have p=p_options[1] and so on…

  • random_state (int or RandomState, default=None) – Controls the random resampling of the original dataset (sample wise and feature wise). If the estimator accepts a random_state attribute, a different seed is generated for each instance in the ensemble. Pass an int for reproducible output across multiple function calls. See Glossary.

n_features_

The number of features when fit() is performed.

Type:

int

estimators_

The collection of fitted estimators.

Type:

list of estimators

estim_features

The subset of drawn features for each estimator.

Type:

list of arrays

Examples

>>> @TODO

References

get_interpretation(directory, base_file_name, y_test, feature_ids, multi_class=False)

Base method that returns an empty string if there is not interpretation method in the classifier’s module

set_fit_request(*, tiebreaker: bool | None | str = '$UNCHANGED$') → ScmBagging

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.

Added 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:

tiebreaker (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for tiebreaker parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_params(p_options=[0.316], **kwargs)

Set the parameters of this estimator.

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

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance

summit.multiview_platform.monoview_classifiers.scm module

class SCM(random_state=None, model_type='conjunction', max_rules=10, p=0.1, **kwargs)

Bases: SetCoveringMachineClassifier, BaseMonoviewClassifier

SCM Classifier :param random_state (default: :type random_state (default: None) :param model_type: :type model_type: string (default: “conjunction”) :param max_rules: :type max_rules: int number maximum of rules (default : 10) :param p: :type p: float value(default : 0.1 ) :param kwarg: :type kwarg: others arguments

param_names
distribs
classed_params
weird_strings
fit(X, y, tiebreaker=None, iteration_callback=None, **fit_params)

Fit a SCM model.

Parameters:

X: array-like, shape=[n_examples, n_features]

The feature of the input examples.

yarray-like, shape = [n_samples]

The labels of the input examples.

tiebreaker: function(model_type, feature_idx, thresholds, rule_type)

A function that takes in the model type and information about the equivalent rules and outputs the index of the rule to use. The lists respectively contain the feature indices, thresholds and type corresponding of the equivalent rules. If None, the rule that most decreases the training error is selected. Note: the model type is provided because the rules that are added to disjunction models correspond to the inverse of the rules that are handled during training. Handle this case with care.

iteration_callback: function(model)

A function that is called each time a rule is added to the model.

Returns:

self: object

Returns self.

get_interpretation(directory, base_file_name, y_test, feature_ids, multi_class=False)

Base method that returns an empty string if there is not interpretation method in the classifier’s module

set_fit_request(*, iteration_callback: bool | None | str = '$UNCHANGED$', tiebreaker: bool | None | str = '$UNCHANGED$') → SCM

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.

Added 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:

  • iteration_callback (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for iteration_callback parameter in fit.

  • tiebreaker (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for tiebreaker parameter in fit.

Returns:

self – The updated object.

Return type:

object

paramsToSet(nIter, random_state)

summit.multiview_platform.monoview_classifiers.sgd module

class SGD(random_state=None, loss='hinge', penalty='l2', alpha=0.0001, max_iter=5, tol=None, **kwargs)

Bases: SGDClassifier, BaseMonoviewClassifier

This class is an adaptation of scikit-learn’s SGDClassifier

set_fit_request(*, coef_init: bool | None | str = '$UNCHANGED$', intercept_init: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$') → SGD

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.

Added 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:

  • coef_init (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for coef_init parameter in fit.

  • intercept_init (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for intercept_init parameter in fit.

  • sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_partial_fit_request(*, classes: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$') → SGD

Request metadata passed to the partial_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 partial_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 partial_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.

Added 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:

  • classes (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for classes parameter in partial_fit.

  • sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in partial_fit.

Returns:

self – The updated object.

Return type:

object

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

summit.multiview_platform.monoview_classifiers.svm_linear module

class SVMLinear(random_state=None, C=1.0, **kwargs)

Bases: SVCClassifier, BaseMonoviewClassifier

This class is an adaptation of scikit-learn’s SVC

Here, it is the linear kernel version

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

summit.multiview_platform.monoview_classifiers.svm_poly module

class SVMPoly(random_state=None, C=1.0, degree=3, **kwargs)

Bases: SVCClassifier, BaseMonoviewClassifier

This class is an adaptation of scikit-learn’s SVC

Here, it is the polynomial kernel version

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

summit.multiview_platform.monoview_classifiers.svm_rbf module

class SVMRBF(random_state=None, C=1.0, **kwargs)

Bases: SVCClassifier, BaseMonoviewClassifier

This class is an adaptation of scikit-learn’s SVC

Here, it is the RBF kernel version

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

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

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.

Added 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_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

Module contents