scikit-learn¶

flowcean.sklearn provides regression learners, scikit-learn model wrappers, and commonly used metrics. The complete classification and regression metric API, including MultiOutputMixin, is available from flowcean.sklearn.metrics.

sklearn ¶

Classes¶

AdaBoost ¶

AdaBoost(estimator: object = None, *, n_estimators: int = 50, learning_rate: float = 1.0, random_state: int | None = None, threshold: float = 0.5)

Bases: SupervisedLearner

Wrapper class for sklearn's AdaBoostClassifier.

Reference: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html

Initialize the AdaBoost classifier learner.

Reference: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html

Attributes¶

classifier `instance-attribute` ¶

classifier: AdaBoostClassifier = AdaBoostClassifier(estimator=estimator, n_estimators=n_estimators, learning_rate=learning_rate, random_state=random_state or get_seed())

threshold `instance-attribute` ¶

threshold = threshold

Methods:¶

learn ¶

learn(inputs: LazyFrame, outputs: LazyFrame) -> Model

LinearRegression ¶

Bases: SupervisedLearner

Methods:¶

learn ¶

learn(inputs: LazyFrame | DataFrame, outputs: LazyFrame | DataFrame) -> SciKitModel

Accuracy ¶

Accuracy(features: list[str] | None = None)

Bases: SelectMixin, LazyMixin, Metric

Accuracy classification score.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`

ClassificationReport ¶

ClassificationReport(features: list[str] | None = None)

Bases: SelectMixin, LazyMixin, Metric

Build a text report showing the main classification metrics.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`

FBetaScore ¶

FBetaScore(*, beta: float = 1.0, features: list[str] | None = None)

Bases: SelectMixin, LazyMixin, Metric

F-beta score.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`beta`	`float`	The beta parameter.	`1.0`
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`

Attributes¶

beta `instance-attribute` ¶

beta = beta

MaxError ¶

MaxError(feature: str | None = None)

Bases: SelectMixin, LazyMixin, Metric

Max error regression loss.

As defined by scikit-learn.

Initialize MaxError metric.

Parameters:

Name	Type	Description	Default
`feature`	`str \| None`	The feature to calculate the metric for. If None, the metric expects a single feature in the data.	`None`

MeanAbsoluteError ¶

MeanAbsoluteError(features: list[str] | None = None, multioutput: Literal['raw_values', 'uniform_average'] = 'raw_values')

Bases: SelectMixin, LazyMixin, MultiOutputMixin, Metric

Mean absolute error (MAE) regression loss.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`
`multioutput`	`Literal['raw_values', 'uniform_average']`	Defines how to aggregate multiple output values. See scikit-learn documentation for details.	`'raw_values'`

MeanAbsolutePercentageError ¶

MeanAbsolutePercentageError(features: list[str] | None = None, multioutput: Literal['raw_values', 'uniform_average'] = 'raw_values')

Bases: SelectMixin, LazyMixin, MultiOutputMixin, Metric

Mean absolute percentage error (MAPE) regression loss.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`
`multioutput`	`Literal['raw_values', 'uniform_average']`	Defines how to aggregate multiple output values. See scikit-learn documentation for details.	`'raw_values'`

MeanSquaredError ¶

MeanSquaredError(features: list[str] | None = None, multioutput: Literal['raw_values', 'uniform_average'] = 'raw_values')

Bases: SelectMixin, LazyMixin, MultiOutputMixin, Metric

Mean squared error (MSE) regression loss.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`
`multioutput`	`Literal['raw_values', 'uniform_average']`	Defines how to aggregate multiple output values. See scikit-learn documentation for details.	`'raw_values'`

PrecisionScore ¶

PrecisionScore(features: list[str] | None = None)

Bases: SelectMixin, LazyMixin, Metric

Precision classification score.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`

R2Score ¶

R2Score(features: list[str] | None = None, multioutput: Literal['raw_values', 'uniform_average'] = 'raw_values')

Bases: SelectMixin, LazyMixin, MultiOutputMixin, Metric

R^2 (coefficient of determination) regression score.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`
`multioutput`	`Literal['raw_values', 'uniform_average']`	Defines how to aggregate multiple output values. See scikit-learn documentation for details.	`'raw_values'`

Recall ¶

Recall(features: list[str] | None = None)

Bases: SelectMixin, LazyMixin, Metric

Recall classification score.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`

SciKitClassifierModel ¶

SciKitClassifierModel(estimator: SupportsPredict, *, output_names: Iterable[str], threshold: float = 0.5, name: str | None = None)

Bases: SciKitModel

A SciKit model for classifiers with probability predictions.

Supports threshold-based predictions via the threshold attribute and exposes class probabilities via predict_proba. The estimator must implement predict_proba.

Initialize the classifier model.

Parameters:

Name	Type	Description	Default
`estimator`	`SupportsPredict`	The scikit-learn classifier (must support `predict_proba`).	required
`output_names`	`Iterable[str]`	The names of the output columns.	required
`threshold`	`float`	Decision threshold for the positive class (default: 0.5).	`0.5`
`name`	`str \| None`	The name of the model.	`None`

Attributes¶

threshold `instance-attribute` ¶

threshold: float = threshold

Methods:¶

predict_proba ¶

predict_proba(input_features: DataFrame | LazyFrame) -> LazyFrame

Predict class probabilities, applying preprocessing transforms.

Parameters:

Name	Type	Description	Default
`input_features`	`DataFrame \| LazyFrame`	The inputs for which to predict probabilities.	required

Returns:

Type	Description
`LazyFrame`	The predicted probabilities for the positive class.

SciKitModel ¶

SciKitModel(estimator: SupportsPredict, *, output_names: Iterable[str], name: str | None = None)

Bases: Model

A model that wraps a scikit-learn estimator.

Initialize the model.

Parameters:

Name	Type	Description	Default
`estimator`	`SupportsPredict`	The scikit-learn estimator.	required
`output_names`	`Iterable[str]`	The names of the output columns.	required
`name`	`str \| None`	The name of the model.	`None`

Attributes¶

estimator `instance-attribute` ¶

estimator: SupportsPredict = estimator

output_names `instance-attribute` ¶

output_names: list[str] = list(output_names)

RandomForestRegressorLearner ¶

RandomForestRegressorLearner(n_estimators: int = 100, *, criterion: str = 'squared_error', max_depth: int | None = None, min_samples_split: int = 2, min_samples_leaf: int = 1, min_weight_fraction_leaf: float = 0.0, max_features: float = 1.0, max_leaf_nodes: int | None = None, min_impurity_decrease: float = 0.0, bootstrap: bool = True, oob_score: bool = False, n_jobs: int | None = None, random_state: int | None = None, verbose: int = 0, warm_start: bool = False, ccp_alpha: float = 0.0, max_samples: int | float | None = None, monotonic_cst: NDArray | None = None, callbacks: list[LearnerCallback] | LearnerCallback | None = None)

Bases: SupervisedLearner

Wrapper class for sklearn's RandomForestRegressor.

Reference: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html

Initialize the random forest learner.

Reference: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html

Parameters:

Name	Type	Description	Default
`n_estimators`	`int`	Number of trees in the forest.	`100`
`criterion`	`str`	Function to measure the quality of a split.	`'squared_error'`
`max_depth`	`int \| None`	Maximum depth of the tree.	`None`
`min_samples_split`	`int`	Minimum number of samples required to split an internal node.	`2`
`min_samples_leaf`	`int`	Minimum number of samples required to be at a leaf node.	`1`
`min_weight_fraction_leaf`	`float`	Minimum weighted fraction of the sum total of weights required to be at a leaf node.	`0.0`
`max_features`	`float`	Number of features to consider when looking for the best split.	`1.0`
`max_leaf_nodes`	`int \| None`	Grow trees with max_leaf_nodes in best-first fashion.	`None`
`min_impurity_decrease`	`float`	A node will be split if this split induces a decrease of the impurity greater than or equal to this value.	`0.0`
`bootstrap`	`bool`	Whether bootstrap samples are used when building trees.	`True`
`oob_score`	`bool`	Whether to use out-of-bag samples to estimate the R^2 on unseen data.	`False`
`n_jobs`	`int \| None`	Number of jobs to run in parallel.	`None`
`random_state`	`int \| None`	Controls the randomness of the estimator.	`None`
`verbose`	`int`	Controls the verbosity when fitting and predicting.	`0`
`warm_start`	`bool`	When set to True, reuse the solution of the previous call to fit.	`False`
`ccp_alpha`	`float`	Complexity parameter used for Minimal Cost-Complexity Pruning.	`0.0`
`max_samples`	`int \| float \| None`	If bootstrap is True, the number of samples to draw from X to train each base estimator.	`None`
`monotonic_cst`	`NDArray \| None`	Monotonicity constraints.	`None`
`callbacks`	`list[LearnerCallback] \| LearnerCallback \| None`	Optional callbacks for progress feedback. Use `None` for silent learning.	`None`

Attributes¶

regressor `instance-attribute` ¶

regressor: RandomForestRegressor = RandomForestRegressor(n_estimators=n_estimators, criterion=criterion, max_depth=max_depth, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, min_weight_fraction_leaf=min_weight_fraction_leaf, max_features=max_features, max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=min_impurity_decrease, bootstrap=bootstrap, oob_score=oob_score, n_jobs=n_jobs, random_state=random_state or get_seed(), verbose=verbose, warm_start=warm_start, ccp_alpha=ccp_alpha, max_samples=max_samples, monotonic_cst=monotonic_cst)

callback_manager `instance-attribute` ¶

callback_manager = create_callback_manager(callbacks)

Methods:¶

learn ¶

learn(inputs: LazyFrame, outputs: LazyFrame) -> Model

Fit the random forest regressor on the given inputs and outputs.

RegressionTree ¶

RegressionTree(*, dot_graph_export_path: str | None = None, criterion: str = 'squared_error', splitter: str = 'best', max_depth: int | None = None, min_samples_split: int = 2, min_samples_leaf: int = 1, min_weight_fraction_leaf: float = 0.0, max_features: float | None = None, random_state: int | None = None, max_leaf_nodes: int | None = None, min_impurity_decrease: float = 0.0, ccp_alpha: float = 0.0, monotonic_cst: NDArray | None = None, callbacks: list[LearnerCallback] | LearnerCallback | None = None)

Bases: SupervisedLearner

Wrapper class for sklearn's DecisionTreeRegressor.

Reference: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html

Initialize the regression tree learner.

Reference: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html

Parameters:

Name	Type	Description	Default
`dot_graph_export_path`	`str \| None`	Path to export the decision tree graph in Graphviz DOT format.	`None`
`criterion`	`str`	Function to measure the quality of a split.	`'squared_error'`
`splitter`	`str`	Strategy used to choose the split at each node.	`'best'`
`max_depth`	`int \| None`	Maximum depth of the tree.	`None`
`min_samples_split`	`int`	Minimum number of samples required to split an internal node.	`2`
`min_samples_leaf`	`int`	Minimum number of samples required to be at a leaf node.	`1`
`min_weight_fraction_leaf`	`float`	Minimum weighted fraction of the sum total of weights required to be at a leaf node.	`0.0`
`max_features`	`float \| None`	Number of features to consider when looking for the best split.	`None`
`random_state`	`int \| None`	Controls the randomness of the estimator.	`None`
`max_leaf_nodes`	`int \| None`	Grow a tree with max_leaf_nodes in best-first fashion.	`None`
`min_impurity_decrease`	`float`	A node will be split if this split induces a decrease of the impurity greater than or equal to this value.	`0.0`
`ccp_alpha`	`float`	Complexity parameter used for Minimal Cost-Complexity Pruning.	`0.0`
`monotonic_cst`	`NDArray \| None`	Monotonicity constraints.	`None`
`callbacks`	`list[LearnerCallback] \| LearnerCallback \| None`	Optional callbacks for progress feedback. Use `None` for silent learning.	`None`

Attributes¶

regressor `instance-attribute` ¶

regressor: DecisionTreeRegressor = DecisionTreeRegressor(criterion=criterion, splitter=splitter, max_depth=max_depth, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, min_weight_fraction_leaf=min_weight_fraction_leaf, max_features=max_features, max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=min_impurity_decrease, random_state=random_state or get_seed(), ccp_alpha=ccp_alpha, monotonic_cst=monotonic_cst)

dot_graph_export_path `instance-attribute` ¶

dot_graph_export_path = dot_graph_export_path

callback_manager `instance-attribute` ¶

callback_manager = create_callback_manager(callbacks)

Methods:¶

learn ¶

learn(inputs: LazyFrame, outputs: LazyFrame) -> Model

Metrics¶

metrics ¶

Classification and regression metrics backed by scikit-learn.

Classes¶

MultiOutputMixin ¶

MultiOutputMixin(multioutput: Literal['raw_values', 'uniform_average', 'variance_weighted'] = 'raw_values', **kwargs: Any)

Mixin to handle sklearn-style multioutput regression metrics.

Attributes¶

multioutput `instance-attribute` ¶

multioutput = multioutput

Accuracy ¶

Accuracy(features: list[str] | None = None)

Bases: SelectMixin, LazyMixin, Metric

Accuracy classification score.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`

ClassificationReport ¶

ClassificationReport(features: list[str] | None = None)

Bases: SelectMixin, LazyMixin, Metric

Build a text report showing the main classification metrics.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`

FBetaScore ¶

FBetaScore(*, beta: float = 1.0, features: list[str] | None = None)

Bases: SelectMixin, LazyMixin, Metric

F-beta score.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`beta`	`float`	The beta parameter.	`1.0`
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`

Attributes¶

beta `instance-attribute` ¶

beta = beta

PrecisionScore ¶

PrecisionScore(features: list[str] | None = None)

Bases: SelectMixin, LazyMixin, Metric

Precision classification score.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`

Recall ¶

Recall(features: list[str] | None = None)

Bases: SelectMixin, LazyMixin, Metric

Recall classification score.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`

MaxError ¶

MaxError(feature: str | None = None)

Bases: SelectMixin, LazyMixin, Metric

Max error regression loss.

As defined by scikit-learn.

Initialize MaxError metric.

Parameters:

Name	Type	Description	Default
`feature`	`str \| None`	The feature to calculate the metric for. If None, the metric expects a single feature in the data.	`None`

MeanAbsoluteError ¶

MeanAbsoluteError(features: list[str] | None = None, multioutput: Literal['raw_values', 'uniform_average'] = 'raw_values')

Bases: SelectMixin, LazyMixin, MultiOutputMixin, Metric

Mean absolute error (MAE) regression loss.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`
`multioutput`	`Literal['raw_values', 'uniform_average']`	Defines how to aggregate multiple output values. See scikit-learn documentation for details.	`'raw_values'`

MeanAbsolutePercentageError ¶

MeanAbsolutePercentageError(features: list[str] | None = None, multioutput: Literal['raw_values', 'uniform_average'] = 'raw_values')

Bases: SelectMixin, LazyMixin, MultiOutputMixin, Metric

Mean absolute percentage error (MAPE) regression loss.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`
`multioutput`	`Literal['raw_values', 'uniform_average']`	Defines how to aggregate multiple output values. See scikit-learn documentation for details.	`'raw_values'`

MeanSquaredError ¶

MeanSquaredError(features: list[str] | None = None, multioutput: Literal['raw_values', 'uniform_average'] = 'raw_values')

Bases: SelectMixin, LazyMixin, MultiOutputMixin, Metric

Mean squared error (MSE) regression loss.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`
`multioutput`	`Literal['raw_values', 'uniform_average']`	Defines how to aggregate multiple output values. See scikit-learn documentation for details.	`'raw_values'`

R2Score ¶

R2Score(features: list[str] | None = None, multioutput: Literal['raw_values', 'uniform_average'] = 'raw_values')

Bases: SelectMixin, LazyMixin, MultiOutputMixin, Metric

R^2 (coefficient of determination) regression score.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
`features`	`list[str] \| None`	The features to calculate the metric for. If None, the metric uses all features in the data.	`None`
`multioutput`	`Literal['raw_values', 'uniform_average']`	Defines how to aggregate multiple output values. See scikit-learn documentation for details.	`'raw_values'`

scikit-learn¶

sklearn ¶

Classes¶

AdaBoost ¶

Attributes¶

classifier instance-attribute ¶

threshold instance-attribute ¶

Methods:¶

learn ¶

LinearRegression ¶

Methods:¶

learn ¶

Accuracy ¶

ClassificationReport ¶

FBetaScore ¶

Attributes¶

beta instance-attribute ¶

MaxError ¶

MeanAbsoluteError ¶

MeanAbsolutePercentageError ¶

MeanSquaredError ¶

PrecisionScore ¶

R2Score ¶

Recall ¶

SciKitClassifierModel ¶

Attributes¶

threshold instance-attribute ¶

Methods:¶

predict_proba ¶

SciKitModel ¶

Attributes¶

estimator instance-attribute ¶

output_names instance-attribute ¶

RandomForestRegressorLearner ¶

Attributes¶

regressor instance-attribute ¶

callback_manager instance-attribute ¶

Methods:¶

learn ¶

RegressionTree ¶

Attributes¶

regressor instance-attribute ¶

dot_graph_export_path instance-attribute ¶

callback_manager instance-attribute ¶

Methods:¶

learn ¶

Metrics¶

metrics ¶

Classes¶

MultiOutputMixin ¶

Attributes¶

multioutput instance-attribute ¶

Accuracy ¶

ClassificationReport ¶

FBetaScore ¶

Attributes¶

beta instance-attribute ¶

PrecisionScore ¶

Recall ¶

MaxError ¶

MeanAbsoluteError ¶

MeanAbsolutePercentageError ¶

MeanSquaredError ¶

R2Score ¶

classifier `instance-attribute` ¶

threshold `instance-attribute` ¶

beta `instance-attribute` ¶

threshold `instance-attribute` ¶

estimator `instance-attribute` ¶

output_names `instance-attribute` ¶

regressor `instance-attribute` ¶

callback_manager `instance-attribute` ¶

regressor `instance-attribute` ¶

dot_graph_export_path `instance-attribute` ¶

callback_manager `instance-attribute` ¶

multioutput `instance-attribute` ¶

beta `instance-attribute` ¶