sklearn.linear_model.RANSACRegressor

class sklearn.linear_model.RANSACRegressor(base_estimator=None, min_samples=None, residual_threshold=None, is_data_valid=None, is_model_valid=None, max_trials=100, stop_n_inliers=inf, stop_score=inf, stop_probability=0.99, residual_metric=None, 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. More information can be found in the general documentation of linear models.

A detailed description of the algorithm can be found in the documentation of the linear_model sub-package.

Parameters:

base_estimator : object, optional

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.

If base_estimator is None, then base_estimator=sklearn.linear_model.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]), optional

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 base_estimator. By default a sklearn.linear_model.LinearRegression() estimator is assumed and min_samples is chosen as X.shape[1] + 1.

residual_threshold : float, optional

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.

is_data_valid : callable, optional

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, optional

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, optional

Maximum number of iterations for random sample selection.

stop_n_inliers : int, optional

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

stop_score : float, optional

Stop iteration if score is greater equal than this threshold.

stop_probability : float in range [0, 1], optional

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.

residual_metric : callable, optional

Metric to reduce the dimensionality of the residuals to 1 for multi-dimensional target values y.shape[1] > 1. By default the sum of absolute differences is used:

lambda dy: np.sum(np.abs(dy), axis=1)

random_state : integer or numpy.RandomState, optional

The generator used to initialize the centers. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.

Attributes:

estimator_ : object

Best fitted model (copy of the base_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.

References

[R136]

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

[R137]

http://www.cs.columbia.edu/~belhumeur/courses/compPhoto/ransac.pdf

[R138]

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

Methods

fit(X, y)	Fit estimator using RANSAC algorithm.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict using the estimated model.
score(X, y)	Returns the score of the prediction.
set_params(**params)	Set the parameters of this estimator.

__init__(base_estimator=None, min_samples=None, residual_threshold=None, is_data_valid=None, is_model_valid=None, max_trials=100, stop_n_inliers=inf, stop_score=inf, stop_probability=0.99, residual_metric=None, random_state=None)

fit(X, y)

Fit estimator using RANSAC algorithm.

Parameters:

X : array-like or sparse matrix, shape [n_samples, n_features]

Training data.

y : array-like, shape = [n_samples] or [n_samples, n_targets]

Target values.

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_params(deep=True)

Get parameters for this estimator.

Parameters:

deep: boolean, optional :

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

Returns:

params : mapping of string to any

Parameter names mapped to their values.

predict(X)

Predict using the estimated model.

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

Parameters:

X : numpy array of shape [n_samples, n_features]

Returns:

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

Returns predicted values.

score(X, y)

Returns the score of the prediction.

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

Parameters:

X : numpy array or sparse matrix of shape [n_samples, n_features]

Training data.

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

Target values.

Returns:

z : float

Score of the prediction.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :