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:
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new TransformedTargetRegressor(opts?: object): TransformedTargetRegressor;Parameters
| Name | 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. Default Value true |
opts.func? | any | Function to apply to y before passing to fit. Cannot be set at the same time as transformer. The function needs to return a 2-dimensional array. If func is None, the function used will be the identity function. |
opts.inverse_func? | any | Function to apply to the prediction of the regressor. Cannot be set at the same time as transformer. The function needs to return a 2-dimensional array. The inverse function is used to return predictions to the same space of the original training labels. |
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
Defined in: generated/compose/TransformedTargetRegressor.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/compose/TransformedTargetRegressor.ts:118 (opens in a new tab)
fit()
Fit the model according to the given training data.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training vector, where n\_samples is the number of samples and n\_features is the number of features. |
opts.fit_params? | any | Parameters passed to the fit method of the underlying regressor. |
opts.y? | ArrayLike | Target values. |
Returns
Promise<any>
Defined in: generated/compose/TransformedTargetRegressor.ts:135 (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/compose/TransformedTargetRegressor.ts:188 (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/compose/TransformedTargetRegressor.ts:70 (opens in a new tab)
predict()
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.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Samples. |
opts.predict_params? | any | Parameters passed to the predict method of the underlying regressor. |
Returns
Promise<ArrayLike>
Defined in: generated/compose/TransformedTargetRegressor.ts:228 (opens in a new tab)
score()
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.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
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.sample_weight? | ArrayLike | Sample weights. |
opts.y? | ArrayLike | True values for X. |
Returns
Promise<number>
Defined in: generated/compose/TransformedTargetRegressor.ts:274 (opens in a new tab)
set_score_request()
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:
Signature
set_score_request(opts: object): Promise<any>;Parameters
| Name | 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:329 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/compose/TransformedTargetRegressor.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/compose/TransformedTargetRegressor.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/compose/TransformedTargetRegressor.ts:21 (opens in a new tab)
id
string
Defined in: generated/compose/TransformedTargetRegressor.ts:18 (opens in a new tab)
opts
any
Defined in: generated/compose/TransformedTargetRegressor.ts:19 (opens in a new tab)
Accessors
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/compose/TransformedTargetRegressor.ts:421 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/compose/TransformedTargetRegressor.ts:57 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/compose/TransformedTargetRegressor.ts:61 (opens in a new tab)
regressor_
Fitted regressor.
Signature
regressor_(): Promise<any>;Returns
Promise<any>
Defined in: generated/compose/TransformedTargetRegressor.ts:367 (opens in a new tab)
transformer_
Transformer used in fit and predict.
Signature
transformer_(): Promise<any>;Returns
Promise<any>
Defined in: generated/compose/TransformedTargetRegressor.ts:394 (opens in a new tab)