Class: TheilSenRegressor

Theil-Sen Estimator: robust multivariate regression model.

The algorithm calculates least square solutions on subsets with size n_subsamples of the samples in X. Any value of n_subsamples between the number of features and samples leads to an estimator with a compromise between robustness and efficiency. Since the number of least square solutions is “n_samples choose n_subsamples”, it can be extremely large and can therefore be limited with max_subpopulation. If this limit is reached, the subsets are chosen randomly. In a final step, the spatial median (or L1 median) is calculated of all least square solutions.

Constructors

new TheilSenRegressor()

new TheilSenRegressor(opts?): TheilSenRegressor

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.copy_X`?	`boolean`	If `true`, X will be copied; else, it may be overwritten.
`opts.fit_intercept`?	`boolean`	Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations.
`opts.max_iter`?	`number`	Maximum number of iterations for the calculation of spatial median.
`opts.max_subpopulation`?	`number`	Instead of computing with a set of cardinality ‘n choose k’, where n is the number of samples and k is the number of subsamples (at least number of features), consider only a stochastic subpopulation of a given maximal size if ‘n choose k’ is larger than max_subpopulation. For other than small problem sizes this parameter will determine memory usage and runtime if n_subsamples is not changed. Note that the data type should be int but floats such as 1e4 can be accepted too.
`opts.n_jobs`?	`number`	Number of CPUs to use during the cross validation. `undefined` means 1 unless in a `joblib.parallel_backend` context. `\-1` means using all processors. See Glossary for more details.
`opts.n_subsamples`?	`number`	Number of samples to calculate the parameters. This is at least the number of features (plus 1 if fit_intercept=`true`) and the number of samples as a maximum. A lower number leads to a higher breakdown point and a low efficiency while a high number leads to a low breakdown point and a high efficiency. If `undefined`, take the minimum number of subsamples leading to maximal robustness. If n_subsamples is set to n_samples, Theil-Sen is identical to least squares.
`opts.random_state`?	`number`	A random number generator instance to define the state of the random permutations generator. Pass an int for reproducible output across multiple function calls. See Glossary.
`opts.tol`?	`number`	Tolerance when calculating spatial median.
`opts.verbose`?	`boolean`	Verbose mode when fitting the model.

Returns TheilSenRegressor

Defined in generated/linear_model/TheilSenRegressor.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/linear_model/TheilSenRegressor.ts:23
`_isInitialized`	`boolean`	`false`	generated/linear_model/TheilSenRegressor.ts:22
`_py`	`PythonBridge`	`undefined`	generated/linear_model/TheilSenRegressor.ts:21
`id`	`string`	`undefined`	generated/linear_model/TheilSenRegressor.ts:18
`opts`	`any`	`undefined`	generated/linear_model/TheilSenRegressor.ts:19

Accessors

breakdown_

Get Signature

get breakdown_(): Promise<number>

Approximated breakdown point.

Returns Promise<number>

Defined in generated/linear_model/TheilSenRegressor.ts:409

coef_

Get Signature

get coef_(): Promise<ArrayLike>

Coefficients of the regression model (median of distribution).

Returns Promise<ArrayLike>

Defined in generated/linear_model/TheilSenRegressor.ts:355

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/TheilSenRegressor.ts:517

intercept_

Get Signature

get intercept_(): Promise<number>

Estimated intercept of regression model.

Returns Promise<number>

Defined in generated/linear_model/TheilSenRegressor.ts:382

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/TheilSenRegressor.ts:490

n_iter_

Get Signature

get n_iter_(): Promise<number>

Number of iterations needed for the spatial median.

Returns Promise<number>

Defined in generated/linear_model/TheilSenRegressor.ts:436

n_subpopulation_

Get Signature

get n_subpopulation_(): Promise<number>

Number of combinations taken into account from ‘n choose k’, where n is the number of samples and k is the number of subsamples.

Returns Promise<number>

Defined in generated/linear_model/TheilSenRegressor.ts:463

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/TheilSenRegressor.ts:87

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/TheilSenRegressor.ts:141

fit()

fit(opts): Promise<any>

Fit linear model.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	Training data.
`opts.y`?	`ArrayLike`	Target values.

Returns Promise<any>

Defined in generated/linear_model/TheilSenRegressor.ts:158

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 `MetadataRequest` encapsulating routing information.

Returns Promise<any>

Defined in generated/linear_model/TheilSenRegressor.ts:199

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/TheilSenRegressor.ts:100

predict()

predict(opts): Promise<any>

Predict using the linear model.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`any`	Samples.

Returns Promise<any>

Defined in generated/linear_model/TheilSenRegressor.ts:235

score()

score(opts): Promise<number>

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true \- y_pred)\*\* 2).sum() and \(v\) is the total sum of squares ((y_true \- y_true.mean()) \*\* 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Sample weights.
`opts.X`?	`ArrayLike`[]	Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape `(n_samples, n_samples_fitted)`, where `n_samples_fitted` is the number of samples used in the fitting for the estimator.
`opts.y`?	`ArrayLike`	True values for `X`.

Returns Promise<number>

Defined in generated/linear_model/TheilSenRegressor.ts:271

set_score_request()

set_score_request(opts): Promise<any>

Request metadata passed to the score 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 `score`.

Returns Promise<any>

Defined in generated/linear_model/TheilSenRegressor.ts:319

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