mindfoundry.optaas.client.sklearn_pipelines.estimators package

Submodules

mindfoundry.optaas.client.sklearn_pipelines.estimators.ada_boost module

class mindfoundry.optaas.client.sklearn_pipelines.estimators.ada_boost.AdaBoostClassifier(base_estimator=None, *, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None)

Bases: AdaBoostClassifier, _OptimizableAdaBoost

make_parameters_constraints_and_prior_means(sk: SklearnParameterMaker, **kwargs) → Tuple[List[Parameter], List[Constraint], List[PriorMeanExpression]]

Generates Parameters, Constraints, and PriorMeans to optimize a AdaBoostClassifier.

class mindfoundry.optaas.client.sklearn_pipelines.estimators.ada_boost.AdaBoostRegressor(base_estimator=None, *, n_estimators=50, learning_rate=1.0, loss='linear', random_state=None)

Bases: AdaBoostRegressor, _OptimizableAdaBoost

make_parameters_constraints_and_prior_means(sk: SklearnParameterMaker, **kwargs) → Tuple[List[Parameter], List[Constraint], List[PriorMeanExpression]]

Generates Parameters, Constraints, and PriorMeans to optimize a AdaBoostRegressor.

mindfoundry.optaas.client.sklearn_pipelines.estimators.ensemble module

class mindfoundry.optaas.client.sklearn_pipelines.estimators.ensemble.DecisionTreeClassifier(*, 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)

Bases: DecisionTreeClassifier, _OptimizableDecisionTree, _OptimizableEnsembleClassifier

Allows us to optimize a DecisionTreeClassifier.

class mindfoundry.optaas.client.sklearn_pipelines.estimators.ensemble.DecisionTreeRegressor(*, criterion='squared_error', 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, ccp_alpha=0.0)

Bases: DecisionTreeRegressor, _OptimizableDecisionTree

Allows us to optimize a DecisionTreeRegressor.

criterion_values: Sequence[str] = ['mse', 'friedman_mse', 'mae', 'squared_error']

min_impurity_decrease_maximum: float = 0.01

class mindfoundry.optaas.client.sklearn_pipelines.estimators.ensemble.ExtraTreesClassifier(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=False, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None)

Bases: ExtraTreesClassifier, _OptimizableEnsembleClassifier, _OptimizableExtraTrees

Allows us to optimize ExtraTreesClassifier estimators.

class mindfoundry.optaas.client.sklearn_pipelines.estimators.ensemble.ExtraTreesRegressor(n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=False, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None)

Bases: ExtraTreesRegressor, _OptimizableEnsembleRegressor, _OptimizableExtraTrees

Allows us to optimize ExtraTreesRegressor estimators.

class mindfoundry.optaas.client.sklearn_pipelines.estimators.ensemble.GradientBoostingClassifier(*, loss='deviance', learning_rate=0.1, n_estimators=100, subsample=1.0, criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, validation_fraction=0.1, n_iter_no_change=None, tol=0.0001, ccp_alpha=0.0)

Bases: GradientBoostingClassifier, _OptimizableGradientBoosting

Allows us to optimize a GradientBoostingClassifier.

make_parameters_constraints_and_prior_means(sk: SklearnParameterMaker, **kwargs) → Tuple[List[Parameter], List[Constraint], List[PriorMeanExpression]]

Generates Parameters, Constraints

and PriorMeans and Constraints to optimize a GradientBoostingClassifier.

Parameters

class_count (int, optional) – Number of classes in the classification dataset. If set to a number other than 2, the loss parameter will not be optimized (because it can only be set to “deviance”).

class mindfoundry.optaas.client.sklearn_pipelines.estimators.ensemble.GradientBoostingRegressor(*, loss='squared_error', learning_rate=0.1, n_estimators=100, subsample=1.0, criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, init=None, random_state=None, max_features=None, alpha=0.9, verbose=0, max_leaf_nodes=None, warm_start=False, validation_fraction=0.1, n_iter_no_change=None, tol=0.0001, ccp_alpha=0.0)

Bases: GradientBoostingRegressor, _OptimizableGradientBoosting

Allows us to optimize a GradientBoostingRegressor.

make_parameters_constraints_and_prior_means(sk: SklearnParameterMaker, **kwargs) → Tuple[List[Parameter], List[Constraint], List[PriorMeanExpression]]

Generates Parameters, Constraints and PriorMeans to optimize a GradientBoostingClassifier.

class mindfoundry.optaas.client.sklearn_pipelines.estimators.ensemble.RandomForestClassifier(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None)

Bases: RandomForestClassifier, _OptimizableEnsembleClassifier, _OptimizableRandomForest

Allows us to optimize RandomForestClassifier estimators.

class mindfoundry.optaas.client.sklearn_pipelines.estimators.ensemble.RandomForestRegressor(n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None)

Bases: RandomForestRegressor, _OptimizableEnsembleRegressor, _OptimizableRandomForest

Allows us to optimize RandomForestRegressor estimators.

mindfoundry.optaas.client.sklearn_pipelines.estimators.ica module

class mindfoundry.optaas.client.sklearn_pipelines.estimators.ica.FastICA(n_components=None, *, algorithm='parallel', whiten=True, fun='logcosh', fun_args=None, max_iter=200, tol=0.0001, w_init=None, random_state=None)

Bases: FastICA, OptimizableBaseEstimator

make_parameters_constraints_and_prior_means(sk: SklearnParameterMaker, **kwargs) → Tuple[List[Parameter], List[Constraint], List[PriorMeanExpression]]

Generates Parameters , Constraints

and PriorMeans to optimize a FastICA estimator.

Parameters

feature_count (int) – Total number of features in your dataset.

mindfoundry.optaas.client.sklearn_pipelines.estimators.k_neighbors module

class mindfoundry.optaas.client.sklearn_pipelines.estimators.k_neighbors.KNeighborsClassifier(n_neighbors=5, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None)

Bases: KNeighborsClassifier, _OptimizableKNeighbors

Allows us to optimize KNeighborsClassifier estimators.

class mindfoundry.optaas.client.sklearn_pipelines.estimators.k_neighbors.KNeighborsRegressor(n_neighbors=5, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None)

Bases: KNeighborsRegressor, _OptimizableKNeighbors

Allows us to optimize KNeighborsRegressor estimators.

mindfoundry.optaas.client.sklearn_pipelines.estimators.linear_model module

class mindfoundry.optaas.client.sklearn_pipelines.estimators.linear_model.Lasso(alpha=1.0, *, fit_intercept=True, normalize='deprecated', precompute=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic')

Bases: Lasso, _OptimizableLinearModel

Allows us to optimize a Lasso estimator.

class mindfoundry.optaas.client.sklearn_pipelines.estimators.linear_model.Ridge(alpha=1.0, *, fit_intercept=True, normalize='deprecated', copy_X=True, max_iter=None, tol=0.001, solver='auto', positive=False, random_state=None)

Bases: Ridge, _OptimizableLinearModel

Allows us to optimize a Ridge estimator.

mindfoundry.optaas.client.sklearn_pipelines.estimators.pca module

class mindfoundry.optaas.client.sklearn_pipelines.estimators.pca.PCA(n_components=None, *, copy=True, whiten=False, svd_solver='auto', tol=0.0, iterated_power='auto', random_state=None)

Bases: PCA, OptimizableBaseEstimator

make_parameters_constraints_and_prior_means(sk: SklearnParameterMaker, **kwargs) → Tuple[List[Parameter], List[Constraint], List[PriorMeanExpression]]

Generates Parameters, Constraints

and PriorMeans to optimize a PCA estimator.

Parameters

feature_count (int) – Total number of features in your dataset.

mindfoundry.optaas.client.sklearn_pipelines.estimators.svc module

class mindfoundry.optaas.client.sklearn_pipelines.estimators.svc.LinearSVC(penalty='l2', loss='squared_hinge', *, dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=1000)

Bases: LinearSVC, _OptimizableSVC

class mindfoundry.optaas.client.sklearn_pipelines.estimators.svc.SVC(*, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=- 1, decision_function_shape='ovr', break_ties=False, random_state=None)

Bases: SVC, _OptimizableSVC

make_parameters_constraints_and_prior_means(sk: SklearnParameterMaker, **kwargs) → Tuple[List[Parameter], List[Constraint], List[PriorMeanExpression]]

Generates Parameters, Constraints and PriorMeans to optimize an SVC estimator.

mindfoundry.optaas.client.sklearn_pipelines.estimators.voting module

class mindfoundry.optaas.client.sklearn_pipelines.estimators.voting.VotingClassifier(estimators, *, voting='hard', weights=None, n_jobs=None, flatten_transform=True, verbose=False)

Bases: VotingClassifier, OptimizableBaseEstimator

make_parameters_constraints_and_prior_means(sk: SklearnParameterMaker, **kwargs) → Tuple[List[Parameter], List[Constraint], List[PriorMeanExpression]]

Generates Parameters, Constraints and PriorMeans to optimize a VotingClassifier.

steps: List[Any]

mindfoundry.optaas.client.sklearn_pipelines.estimators.xgboost module

class mindfoundry.optaas.client.sklearn_pipelines.estimators.xgboost.XGBClassifier(*, objective: Optional[Union[str, Callable[[ndarray, ndarray], Tuple[ndarray, ndarray]]]] = 'binary:logistic', use_label_encoder: bool = True, **kwargs: Any)

Bases: XGBClassifier, OptimizableBaseEstimator

make_parameters_constraints_and_prior_means(sk: SklearnParameterMaker, **kwargs) → Tuple[List[Parameter], List[Constraint], List[PriorMeanExpression]]

Generates Parameters, Constraints

and PriorMeans to optimize a XGBClassifier estimator.

Parameters

gpu_enabled (bool, optional, default=False) – If True, the objective parameter will include gpu-specific values such as ‘gpu:reg:linear’.

Module contents