Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

# `sklearn.linear_model`.RANSACRegressor

class sklearn.linear_model.RANSACRegressor(estimator=None, *, min_samples=None, residual_threshold=None, is_data_valid=None, is_model_valid=None, max_trials=100, max_skips=inf, stop_n_inliers=inf, stop_score=inf, stop_probability=0.99, loss='absolute_error', random_state=None)

  RANSAC (RANdom SAmple Consensus) algorithm.

  RANSAC is an iterative algorithm for the robust estimation of parameters from a subset of inliers from the complete data set.

  Read more in the User Guide.

  Parameters:
    estimator object, default=None

      Base estimator object which implements the following methods:

      > - `fit(X, y)`: Fit model to given training data and target values.

      > - `score(X, y)`: Returns the mean accuracy on the given test data, which is used for the stop criterion defined by `stop_score`. Additionally, the score is used to decide which of two equally large consensus sets is chosen as the better one.

      > - `predict(X)`: Returns predicted values using the linear model, which is used to compute residual error using loss function.

      If `estimator` is None, then `LinearRegression` is used for target values of dtype float.

      Note that the current implementation only supports regression estimators.

    min_samples int (>= 1) or float ([0, 1]), default=None

      Minimum number of samples chosen randomly from original data. Treated as an absolute number of samples for `min_samples >= 1`, treated as a relative number `ceil(min_samples * X.shape[0])` for `min_samples < 1`. This is typically chosen as the minimal number of samples necessary to estimate the given `estimator`. By default a `LinearRegression` estimator is assumed and `min_samples` is chosen as `X.shape[1] + 1`. This parameter is highly dependent upon the model, so if a `estimator` other than `LinearRegression` is used, the user must provide a value.

    residual_threshold float, default=None

      Maximum residual for a data sample to be classified as an inlier. By default the threshold is chosen as the MAD (median absolute deviation) of the target values `y`. Points whose residuals are strictly equal to the threshold are considered as inliers.

    is_data_valid callable, default=None

      This function is called with the randomly selected data before the model is fitted to it: `is_data_valid(X, y)`. If its return value is False the current randomly chosen sub-sample is skipped.

    is_model_valid callable, default=None

      This function is called with the estimated model and the randomly selected data: `is_model_valid(model, X, y)`. If its return value is False the current randomly chosen sub-sample is skipped. Rejecting samples with this function is computationally costlier than with `is_data_valid`. `is_model_valid` should therefore only be used if the estimated model is needed for making the rejection decision.

    max_trials int, default=100

      Maximum number of iterations for random sample selection.

    max_skips int, default=np.inf

      Maximum number of iterations that can be skipped due to finding zero inliers or invalid data defined by `is_data_valid` or invalid models defined by `is_model_valid`.

      New in version 0.19.

    stop_n_inliers int, default=np.inf

      Stop iteration if at least this number of inliers are found.

    stop_score float, default=np.inf

      Stop iteration if score is greater equal than this threshold.

    stop_probability float in range [0, 1], default=0.99

      RANSAC iteration stops if at least one outlier-free set of the training data is sampled in RANSAC. This requires to generate at least N samples (iterations):

          N >= log(1 - probability) / log(1 - e**m)

      where the probability (confidence) is typically set to high value such as 0.99 (the default) and e is the current fraction of inliers w.r.t. the total number of samples.

    loss str, callable, default=’absolute_error’

      String inputs, ‘absolute_error’ and ‘squared_error’ are supported which find the absolute error and squared error per sample respectively.

      If `loss` is a callable, then it should be a function that takes two arrays as inputs, the true and predicted value and returns a 1-D array with the i-th value of the array corresponding to the loss on `X[i]`.

      If the loss on a sample is greater than the `residual_threshold`, then this sample is classified as an outlier.

      New in version 0.18.

    random_state int, RandomState instance, default=None

      The generator used to initialize the centers. Pass an int for reproducible output across multiple function calls. See Glossary.

  Attributes:
    estimator_object

      Best fitted model (copy of the `estimator` object).

    n_trials_int

      Number of random selection trials until one of the stop criteria is met. It is always `<= max_trials`.

    inlier_mask_bool array of shape [n_samples]

      Boolean mask of inliers classified as `True`.

    n_skips_no_inliers_int

      Number of iterations skipped due to finding zero inliers.

      New in version 0.19.

    n_skips_invalid_data_int

      Number of iterations skipped due to invalid data defined by `is_data_valid`.

      New in version 0.19.

    n_skips_invalid_model_int

      Number of iterations skipped due to an invalid model defined by `is_model_valid`.

      New in version 0.19.

    n_features_in_int

      Number of features seen during fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 1.0.

  See also

  `HuberRegressor`

    Linear regression model that is robust to outliers.

  `TheilSenRegressor`

    Theil-Sen Estimator robust multivariate regression model.

  `SGDRegressor`

    Fitted by minimizing a regularized empirical loss with SGD.

  References

  [1]

  https://en.wikipedia.org/wiki/RANSAC

  [2]

  https://www.sri.com/wp-content/uploads/2021/12/ransac-publication.pdf

  [3]

  http://www.bmva.org/bmvc/2009/Papers/Paper355/Paper355.pdf

  Examples

      >>> from sklearn.linear_model import RANSACRegressor
      >>> from sklearn.datasets import make_regression
      >>> X, y = make_regression(
      ...     n_samples=200, n_features=2, noise=4.0, random_state=0)
      >>> reg = RANSACRegressor(random_state=0).fit(X, y)
      >>> reg.score(X, y)
      0.9885...
      >>> reg.predict(X[:1,])
      array([-31.9417...])

  Methods

  `fit`(X, y[, sample_weight]) | Fit estimator using RANSAC algorithm.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `predict`(X) | Predict using the estimated model.
  `score`(X, y) | Return the score of the prediction.
  `set_fit_request`(*[, sample_weight]) | Request metadata passed to the `fit` method.
  `set_params`(**params) | Set the parameters of this estimator.

  fit(X, y, sample_weight=None)

    Fit estimator using RANSAC algorithm.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Training data.

      y array-like of shape (n_samples,) or (n_samples, n_targets)

        Target values.

      sample_weight array-like of shape (n_samples,), default=None

        Individual weights for each sample raises error if sample_weight is passed and estimator fit method does not support it.

        New in version 0.18.

    Returns:
      self object

        Fitted `RANSACRegressor` estimator.

    Raises:
      ValueError

        If no valid consensus set could be found. This occurs if `is_data_valid` and `is_model_valid` return False for all `max_trials` randomly chosen sub-samples.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  predict(X)

    Predict using the estimated model.

    This is a wrapper for `estimator_.predict(X)`.

    Parameters:
      X{array-like or sparse matrix} of shape (n_samples, n_features)

        Input data.

    Returns:
      y array, shape = [n_samples] or [n_samples, n_targets]

        Returns predicted values.

  score(X, y)

    Return the score of the prediction.

    This is a wrapper for `estimator_.score(X, y)`.

    Parameters:
      X(array-like or sparse matrix} of shape (n_samples, n_features)

        Training data.

      y array-like of shape (n_samples,) or (n_samples, n_targets)

        Target values.

    Returns:
      z float

        Score of the prediction.

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

    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.

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

        Metadata routing for `sample_weight` parameter in `fit`.

    Returns:
      self object

        The updated object.

  set_params(**params)

    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

        Estimator instance.

## Examples using `sklearn.linear_model.RANSACRegressor`

Robust linear estimator fitting

Robust linear estimator fitting

Robust linear model estimation using RANSAC

Robust linear model estimation using RANSAC

Theil-Sen Regression

Theil-Sen Regression