Class: 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.
Constructors
new RANSACRegressor()
new RANSACRegressor(
opts?):RANSACRegressor
Parameters
| Parameter | 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. |
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. |
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): |
opts.stop_score? | number | Stop iteration if score is greater equal than this threshold. |
Returns RANSACRegressor
Defined in generated/linear_model/RANSACRegressor.ts:25
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/linear_model/RANSACRegressor.ts:23 |
_isInitialized | boolean | false | generated/linear_model/RANSACRegressor.ts:22 |
_py | PythonBridge | undefined | generated/linear_model/RANSACRegressor.ts:21 |
id | string | undefined | generated/linear_model/RANSACRegressor.ts:18 |
opts | any | undefined | generated/linear_model/RANSACRegressor.ts:19 |
Accessors
estimator_
Get Signature
get estimator_():
Promise<any>
Best fitted model (copy of the estimator object).
Returns Promise<any>
Defined in generated/linear_model/RANSACRegressor.ts:373
feature_names_in_
Get Signature
get feature_names_in_():
Promise<ArrayLike>
Names of features seen during fit. Defined only when X has feature names that are all strings.
Returns Promise<ArrayLike>
Defined in generated/linear_model/RANSACRegressor.ts:548
inlier_mask_
Get Signature
get inlier_mask_():
Promise<any>
Boolean mask of inliers classified as true.
Returns Promise<any>
Defined in generated/linear_model/RANSACRegressor.ts:423
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/linear_model/RANSACRegressor.ts:523
n_skips_invalid_data_
Get Signature
get n_skips_invalid_data_():
Promise<number>
Number of iterations skipped due to invalid data defined by is_data_valid.
Returns Promise<number>
Defined in generated/linear_model/RANSACRegressor.ts:473
n_skips_invalid_model_
Get Signature
get n_skips_invalid_model_():
Promise<number>
Number of iterations skipped due to an invalid model defined by is_model_valid.
Returns Promise<number>
Defined in generated/linear_model/RANSACRegressor.ts:498
n_skips_no_inliers_
Get Signature
get n_skips_no_inliers_():
Promise<number>
Number of iterations skipped due to finding zero inliers.
Returns Promise<number>
Defined in generated/linear_model/RANSACRegressor.ts:448
n_trials_
Get Signature
get n_trials_():
Promise<number>
Number of random selection trials until one of the stop criteria is met. It is always <= max_trials.
Returns Promise<number>
Defined in generated/linear_model/RANSACRegressor.ts:398
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/linear_model/RANSACRegressor.ts:100
Methods
dispose()
dispose():
Promise<void>
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Returns Promise<void>
Defined in generated/linear_model/RANSACRegressor.ts:152
fit()
fit(
opts):Promise<any>
Fit estimator using RANSAC algorithm.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.fit_params? | any | Parameters routed to the fit method of the sub-estimator via the metadata routing API. |
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.X? | ArrayLike | Training data. |
opts.y? | ArrayLike | Target values. |
Returns Promise<any>
Defined in generated/linear_model/RANSACRegressor.ts:169
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRouter encapsulating routing information. |
Returns Promise<any>
Defined in generated/linear_model/RANSACRegressor.ts:218
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/linear_model/RANSACRegressor.ts:113
predict()
predict(
opts):Promise<any>
Predict using the estimated model.
This is a wrapper for estimator_.predict(X).
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.params? | any | Parameters routed to the predict method of the sub-estimator via the metadata routing API. |
opts.X? | any[] | Input data. |
Returns Promise<any>
Defined in generated/linear_model/RANSACRegressor.ts:254
score()
score(
opts):Promise<number>
Return the score of the prediction.
This is a wrapper for estimator_.score(X, y).
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.params? | any | Parameters routed to the score method of the sub-estimator via the metadata routing API. |
opts.X? | any[] | Training data. |
opts.y? | ArrayLike | Target values. |
Returns Promise<number>
Defined in generated/linear_model/RANSACRegressor.ts:293
set_fit_request()
set_fit_request(
opts):Promise<any>
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:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in fit. |
Returns Promise<any>