RANSACRegressor
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.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new RANSACRegressor(opts?: object): RANSACRegressor;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.estimator? | any | Base estimator object which implements the following methods: |
opts.is_data_valid? | any | 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. |
opts.is_model_valid? | any | 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. |
opts.loss? | string | 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. Default Value 'absolute_error' |
opts.max_skips? | number | 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. |
opts.max_trials? | number | Maximum number of iterations for random sample selection. Default Value 100 |
opts.min_samples? | number | 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. |
opts.random_state? | number | The generator used to initialize the centers. Pass an int for reproducible output across multiple function calls. See Glossary. |
opts.residual_threshold? | number | 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. |
opts.stop_n_inliers? | number | Stop iteration if at least this number of inliers are found. |
opts.stop_probability? | number | 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): Default Value 0.99 |
opts.stop_score? | number | Stop iteration if score is greater equal than this threshold. |
Returns
Defined in: generated/linear_model/RANSACRegressor.ts:25 (opens in a new tab)
Methods
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/linear_model/RANSACRegressor.ts:171 (opens in a new tab)
fit()
Fit estimator using RANSAC algorithm.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training data. |
opts.sample_weight? | ArrayLike | Individual weights for each sample raises error if sample_weight is passed and estimator fit method does not support it. |
opts.y? | ArrayLike | Target values. |
Returns
Promise<any>
Defined in: generated/linear_model/RANSACRegressor.ts:188 (opens in a new tab)
get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Signature
get_metadata_routing(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns
Promise<any>
Defined in: generated/linear_model/RANSACRegressor.ts:237 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/linear_model/RANSACRegressor.ts:113 (opens in a new tab)
predict()
Predict using the estimated model.
This is a wrapper for estimator\_.predict(X).
Signature
predict(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any[] | Input data. |
Returns
Promise<any>
Defined in: generated/linear_model/RANSACRegressor.ts:274 (opens in a new tab)
score()
Return the score of the prediction.
This is a wrapper for estimator\_.score(X, y).
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any[] | Training data. |
opts.y? | ArrayLike | Target values. |
Returns
Promise<number>
Defined in: generated/linear_model/RANSACRegressor.ts:309 (opens in a new tab)
set_fit_request()
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:
Signature
set_fit_request(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in fit. |
Returns
Promise<any>
Defined in: generated/linear_model/RANSACRegressor.ts:353 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/linear_model/RANSACRegressor.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/linear_model/RANSACRegressor.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/linear_model/RANSACRegressor.ts:21 (opens in a new tab)
id
string
Defined in: generated/linear_model/RANSACRegressor.ts:18 (opens in a new tab)
opts
any
Defined in: generated/linear_model/RANSACRegressor.ts:19 (opens in a new tab)
Accessors
estimator_
Best fitted model (copy of the estimator object).
Signature
estimator_(): Promise<any>;Returns
Promise<any>
Defined in: generated/linear_model/RANSACRegressor.ts:388 (opens in a new tab)
feature_names_in_
Names of features seen during fit. Defined only when X has feature names that are all strings.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/linear_model/RANSACRegressor.ts:563 (opens in a new tab)
inlier_mask_
Boolean mask of inliers classified as true.
Signature
inlier_mask_(): Promise<any>;Returns
Promise<any>
Defined in: generated/linear_model/RANSACRegressor.ts:438 (opens in a new tab)
n_features_in_
Number of features seen during fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/RANSACRegressor.ts:538 (opens in a new tab)
n_skips_invalid_data_
Number of iterations skipped due to invalid data defined by is\_data\_valid.
Signature
n_skips_invalid_data_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/RANSACRegressor.ts:488 (opens in a new tab)
n_skips_invalid_model_
Number of iterations skipped due to an invalid model defined by is\_model\_valid.
Signature
n_skips_invalid_model_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/RANSACRegressor.ts:513 (opens in a new tab)
n_skips_no_inliers_
Number of iterations skipped due to finding zero inliers.
Signature
n_skips_no_inliers_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/RANSACRegressor.ts:463 (opens in a new tab)
n_trials_
Number of random selection trials until one of the stop criteria is met. It is always <= max\_trials.
Signature
n_trials_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/RANSACRegressor.ts:413 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/linear_model/RANSACRegressor.ts:100 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/linear_model/RANSACRegressor.ts:104 (opens in a new tab)