art.estimators.regression.scikitlearn¶
This module implements the regressors for scikit-learn models.
Base Class Scikit-learn¶
- class art.estimators.regression.scikitlearn.ScikitlearnRegressor(model: sklearn.base.BaseEstimator, clip_values: Optional[CLIP_VALUES_TYPE] = None, preprocessing_defences: Optional[Union[Preprocessor, List[Preprocessor]]] = None, postprocessing_defences: Optional[Union[Postprocessor, List[Postprocessor]]] = None, preprocessing: PREPROCESSING_TYPE = (0.0, 1.0))¶
Wrapper class for scikit-learn regression models.
- clone_for_refitting() ScikitlearnRegressor¶
Create a copy of the classifier that can be refit from scratch.
- Returns
new estimator
- compute_loss(x: ndarray, y: ndarray, **kwargs) ndarray¶
Compute the MSE loss of the regressor for samples x.
- Return type
ndarray- Parameters
x (
ndarray) – Input samples.y (
ndarray) – Target values.
- Returns
Loss values.
- compute_loss_from_predictions(pred: ndarray, y: ndarray, **kwargs) ndarray¶
Compute the MSE loss of the regressor for predictions pred.
- Return type
ndarray- Parameters
pred (
ndarray) – Model predictions.y (
ndarray) – Target values.
- Returns
Loss values.
- fit(x: ndarray, y: ndarray, **kwargs) None¶
Fit the regressor on the training set (x, y).
- Parameters
x (
ndarray) – Training data.y (
ndarray) – Target values.kwargs – Dictionary of framework-specific arguments. These should be parameters supported by the fit function in sklearn regressor and will be passed to this function as such.
- property input_shape: Tuple[int, ...]¶
Return the shape of one input sample.
- Returns
Shape of one input sample.
- predict(x: ndarray, **kwargs) ndarray¶
Perform prediction for a batch of inputs.
- Return type
ndarray- Parameters
x (
ndarray) – Input samples.- Returns
Array of predictions.
- Raises
ValueError – If the regressor does not have the method predict
- reset() None¶
Resets the weights of the classifier so that it can be refit from scratch.
- save(filename: str, path: Optional[str] = None) None¶
Save a model to file in the format specific to the backend framework.
- Parameters
filename (
str) – Name of the file where to store the model.path – Path of the folder where to store the model. If no path is specified, the model will be stored in the default data location of the library ART_DATA_PATH.
Scikit-learn Decision Tree Regressor¶
- class art.estimators.regression.scikitlearn.ScikitlearnDecisionTreeRegressor(model: sklearn.tree.DecisionTreeRegressor, clip_values: Optional[CLIP_VALUES_TYPE] = None, preprocessing_defences: Optional[Union[Preprocessor, List[Preprocessor]]] = None, postprocessing_defences: Optional[Union[Postprocessor, List[Postprocessor]]] = None, preprocessing: PREPROCESSING_TYPE = (0.0, 1.0))¶
Wrapper class for scikit-learn Decision Tree Regressor models.
- __init__(model: sklearn.tree.DecisionTreeRegressor, clip_values: Optional[CLIP_VALUES_TYPE] = None, preprocessing_defences: Optional[Union[Preprocessor, List[Preprocessor]]] = None, postprocessing_defences: Optional[Union[Postprocessor, List[Postprocessor]]] = None, preprocessing: PREPROCESSING_TYPE = (0.0, 1.0)) None¶
Create a Regressor instance from a scikit-learn Decision Tree Regressor model.
- Parameters
model – scikit-learn Decision Tree Regressor model.
clip_values – Tuple of the form (min, max) representing the minimum and maximum values allowed for features.
preprocessing_defences – Preprocessing defence(s) to be applied by the classifier.
postprocessing_defences – Postprocessing defence(s) to be applied by the classifier.
preprocessing – Tuple of the form (subtrahend, divisor) of floats or np.ndarray of values to be used for data preprocessing. The first value will be subtracted from the input. The input will then be divided by the second one.
- property clip_values: Optional[CLIP_VALUES_TYPE]¶
Return the clip values of the input samples.
- Returns
Clip values (min, max).
- clone_for_refitting() ScikitlearnRegressor¶
Create a copy of the classifier that can be refit from scratch.
- Returns
new estimator
- compute_loss(x: ndarray, y: ndarray, **kwargs) ndarray¶
Compute the MSE loss of the regressor for samples x.
- Return type
ndarray- Parameters
x (
ndarray) – Input samples.y (
ndarray) – Target values.
- Returns
Loss values.
- compute_loss_from_predictions(pred: ndarray, y: ndarray, **kwargs) ndarray¶
Compute the MSE loss of the regressor for predictions pred.
- Return type
ndarray- Parameters
pred (
ndarray) – Model predictions.y (
ndarray) – Target values.
- Returns
Loss values.
- fit(x: ndarray, y: ndarray, **kwargs) None¶
Fit the regressor on the training set (x, y).
- Parameters
x (
ndarray) – Training data.y (
ndarray) – Target values.kwargs – Dictionary of framework-specific arguments. These should be parameters supported by the fit function in sklearn regressor and will be passed to this function as such.
- get_decision_path(x: ndarray) ndarray¶
Returns the path through nodes in the tree when classifying x. Last one is leaf, first one root node.
- Returns
The indices of the nodes in the array structure of the tree.
- get_feature_at_node(node_id: int) int¶
Returns the feature of given id for a node.
- Returns
Feature index of feature split in this node.
- get_left_child(node_id: int) int¶
Returns the id of the left child node of node_id.
- Returns
The indices of the left child in the tree.
- get_params() Dict[str, Any]¶
Get all parameters and their values of this estimator.
- Returns
A dictionary of string parameter names to their value.
- get_right_child(node_id: int) int¶
Returns the id of the right child node of node_id.
- Returns
The indices of the right child in the tree.
- get_samples_at_node(node_id: int) int¶
Returns the number of training samples mapped to a node.
- Returns
Number of samples mapped this node.
- get_threshold_at_node(node_id: int) float¶
Returns the threshold of given id for a node.
- Returns
Threshold value of feature split in this node.
- get_values_at_node(node_id: int) ndarray¶
Returns the feature of given id for a node.
- Returns
Normalized values at node node_id.
- property input_shape: Tuple[int, ...]¶
Return the shape of one input sample.
- Returns
Shape of one input sample.
- property model¶
Return the model.
- Returns
The model.
- predict(x: ndarray, **kwargs) ndarray¶
Perform prediction for a batch of inputs.
- Return type
ndarray- Parameters
x (
ndarray) – Input samples.- Returns
Array of predictions.
- Raises
ValueError – If the regressor does not have the method predict
- reset() None¶
Resets the weights of the classifier so that it can be refit from scratch.
- save(filename: str, path: Optional[str] = None) None¶
Save a model to file in the format specific to the backend framework.
- Parameters
filename (
str) – Name of the file where to store the model.path – Path of the folder where to store the model. If no path is specified, the model will be stored in the default data location of the library ART_DATA_PATH.
- set_params(**kwargs) None¶
Take a dictionary of parameters and apply checks before setting them as attributes.
- Parameters
kwargs – A dictionary of attributes.