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)[source]#

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:

estimatorobject, 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_samplesint (>= 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_thresholdfloat, 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_validcallable, 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_validcallable, 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_trialsint, default=100

Maximum number of iterations for random sample selection.

max_skipsint, 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.

Added in version 0.19.

stop_n_inliersint, default=np.inf

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

stop_scorefloat, default=np.inf

Stop iteration if score is greater equal than this threshold.

stop_probabilityfloat 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 &gt;= 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.

lossstr, 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.

Added in version 0.18.

random_stateint, 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.

Added in version 0.19.
n_skips_invalid_data_int: Number of iterations skipped due to invalid data defined by is_data_valid.

Added in version 0.19.
n_skips_invalid_model_int: Number of iterations skipped due to an invalid model defined by is_model_valid.

Added in version 0.19.
n_features_in_int: Number of features seen during fit.

Added 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.

Added 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

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

For a more detailed example, see Robust linear model estimation using RANSAC

fit(X, y, *, sample_weight=None, **fit_params)[source]#

Fit estimator using RANSAC algorithm.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Training data.
yarray-like of shape (n_samples,) or (n_samples, n_targets): Target values.
sample_weightarray-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.

Added in version 0.18.
**fit_paramsdict: Parameters routed to the fit method of the sub-estimator via the metadata routing API.

Added in version 1.5: Only available if sklearn.set_config(enable_metadata_routing=True) is set. See Metadata Routing User guide for more details.

Returns:

selfobject: 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()[source]#

get metadata routing of this object.

Please check User guide on how the routing mechanism works.

Added in version 1.5.

Returns:

routingMetadataRouter: A MetadataRouter encapsulating routing information.

get_params(deep=True)[source]#

get parameters for this estimator.

Parameters:

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

Returns:

paramsdict: Parameter names mapped to their values.

predict(X, **params)[source]#

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.
**paramsdict: Parameters routed to the predict method of the sub-estimator via the metadata routing API.

Added in version 1.5: Only available if sklearn.set_config(enable_metadata_routing=True) is set. See Metadata Routing User guide for more details.

Returns:

yarray, shape = [n_samples] or [n_samples, n_targets]: Returns predicted values.

score(X, y, **params)[source]#

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.
yarray-like of shape (n_samples,) or (n_samples, n_targets): Target values.
**paramsdict: Parameters routed to the score method of the sub-estimator via the metadata routing API.

Added in version 1.5: Only available if sklearn.set_config(enable_metadata_routing=True) is set. See Metadata Routing User guide for more details.

Returns:

zfloat: Score of the prediction.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANgED$') → RANSACRegressor[source]#

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_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANgED: Metadata routing for sample_weight parameter in fit.

Returns:

selfobject: The updated object.

set_params(**params)[source]#

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:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

gallery examples#

Robust linear estimator fitting

Robust linear model estimation using RANSAC

Theil-Sen Regression