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, 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
- base_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 bystop_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
base_estimator
is None, thenLinearRegression
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 numberceil(min_samples * X.shape[0]
) formin_samples < 1
. This is typically chosen as the minimal number of samples necessary to estimate the givenbase_estimator
. By default asklearn.linear_model.LinearRegression()
estimator is assumed andmin_samples
is chosen asX.shape[1] + 1
.- 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
.- 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 withis_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 byis_model_valid
.New 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 >= 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.
- lossstring, 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 onX[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.
Deprecated since version 1.0: The loss ‘squared_loss’ was deprecated in v1.0 and will be removed in version 1.2. Use
loss='squared_error'
which is equivalent.Deprecated since version 1.0: The loss ‘absolute_loss’ was deprecated in v1.0 and will be removed in version 1.2. Use
loss='absolute_error'
which is equivalent.- 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
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
.- 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.
References
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_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.
- fit(X, y, sample_weight=None)[source]¶
Fit estimator using RANSAC algorithm.
- Parameters
- Xarray-like or sparse matrix, 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 base_estimator fit method does not support it.
New in version 0.18.
- Raises
- ValueError
If no valid consensus set could be found. This occurs if
is_data_valid
andis_model_valid
return False for allmax_trials
randomly chosen sub-samples.
- 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)[source]¶
Predict using the estimated model.
This is a wrapper for
estimator_.predict(X)
.- Parameters
- Xnumpy array of shape [n_samples, n_features]
- Returns
- yarray, shape = [n_samples] or [n_samples, n_targets]
Returns predicted values.
- score(X, y)[source]¶
Returns the score of the prediction.
This is a wrapper for
estimator_.score(X, y)
.- Parameters
- Xnumpy array or sparse matrix of shape [n_samples, n_features]
Training data.
- yarray, shape = [n_samples] or [n_samples, n_targets]
Target values.
- Returns
- zfloat
Score of the prediction.
- 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.