Class: TransformedTargetRegressor
Meta-estimator to regress on a transformed target.
Useful for applying a non-linear transformation to the target y in regression problems. This transformation can be given as a Transformer such as the QuantileTransformer or as a function and its inverse such as np.log and np.exp.
The computation during fit is:
Constructors
new TransformedTargetRegressor()
new TransformedTargetRegressor(
opts?):TransformedTargetRegressor
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.check_inverse? | boolean | Whether to check that transform followed by inverse_transform or func followed by inverse_func leads to the original targets. |
opts.func? | any | Function to apply to y before passing to fit. Cannot be set at the same time as transformer. If func is None, the function used will be the identity function. If func is set, inverse_func also needs to be provided. The function needs to return a 2-dimensional array. |
opts.inverse_func? | any | Function to apply to the prediction of the regressor. Cannot be set at the same time as transformer. The inverse function is used to return predictions to the same space of the original training labels. If inverse_func is set, func also needs to be provided. The inverse function needs to return a 2-dimensional array. |
opts.regressor? | any | Regressor object such as derived from RegressorMixin. This regressor will automatically be cloned each time prior to fitting. If regressor is None, LinearRegression is created and used. |
opts.transformer? | any | Estimator object such as derived from TransformerMixin. Cannot be set at the same time as func and inverse_func. If transformer is None as well as func and inverse_func, the transformer will be an identity transformer. Note that the transformer will be cloned during fitting. Also, the transformer is restricting y to be a numpy array. |
Returns TransformedTargetRegressor
Defined in generated/compose/TransformedTargetRegressor.ts:25
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/compose/TransformedTargetRegressor.ts:23 |
_isInitialized | boolean | false | generated/compose/TransformedTargetRegressor.ts:22 |
_py | PythonBridge | undefined | generated/compose/TransformedTargetRegressor.ts:21 |
id | string | undefined | generated/compose/TransformedTargetRegressor.ts:18 |
opts | any | undefined | generated/compose/TransformedTargetRegressor.ts:19 |
Accessors
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/compose/TransformedTargetRegressor.ts:393
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/compose/TransformedTargetRegressor.ts:57
regressor_
Get Signature
get regressor_():
Promise<any>
Fitted regressor.
Returns Promise<any>
Defined in generated/compose/TransformedTargetRegressor.ts:339
transformer_
Get Signature
get transformer_():
Promise<any>
Transformer used in fit and predict.
Returns Promise<any>
Defined in generated/compose/TransformedTargetRegressor.ts:366
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/compose/TransformedTargetRegressor.ts:113
fit()
fit(
opts):Promise<any>
Fit the model according to the given training data.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.fit_params? | any | Parameters passed to the fit method of the underlying regressor. |
opts.X? | ArrayLike | Training vector, where n_samples is the number of samples and n_features is the number of features. |
opts.y? | ArrayLike | Target values. |
Returns Promise<any>
Defined in generated/compose/TransformedTargetRegressor.ts:130
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Raise NotImplementedError.
This estimator does not support metadata routing yet.
Parameters
| Parameter | Type |
|---|---|
opts | object |
Returns Promise<any>
Defined in generated/compose/TransformedTargetRegressor.ts:178
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/compose/TransformedTargetRegressor.ts:70
predict()
predict(
opts):Promise<ArrayLike>
Predict using the base regressor, applying inverse.
The regressor is used to predict and the inverse_func or inverse_transform is applied before returning the prediction.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.predict_params? | any | Parameters passed to the predict method of the underlying regressor. |
opts.X? | ArrayLike | Samples. |
Returns Promise<ArrayLike>
Defined in generated/compose/TransformedTargetRegressor.ts:210
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/compose/TransformedTargetRegressor.ts:253
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/compose/TransformedTargetRegressor.ts:303