QuantileTransformer
Transform features using quantiles information.
This method transforms the features to follow a uniform or a normal distribution. Therefore, for a given feature, this transformation tends to spread out the most frequent values. It also reduces the impact of (marginal) outliers: this is therefore a robust preprocessing scheme.
The transformation is applied on each feature independently. First an estimate of the cumulative distribution function of a feature is used to map the original values to a uniform distribution. The obtained values are then mapped to the desired output distribution using the associated quantile function. Features values of new/unseen data that fall below or above the fitted range will be mapped to the bounds of the output distribution. Note that this transform is non-linear. It may distort linear correlations between variables measured at the same scale but renders variables measured at different scales more directly comparable.
For example visualizations, refer to Compare QuantileTransformer with other scalers.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new QuantileTransformer(opts?: object): QuantileTransformer;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.copy? | boolean | Set to false to perform inplace transformation and avoid a copy (if the input is already a numpy array). Default Value true |
opts.ignore_implicit_zeros? | boolean | Only applies to sparse matrices. If true, the sparse entries of the matrix are discarded to compute the quantile statistics. If false, these entries are treated as zeros. Default Value false |
opts.n_quantiles? | number | Number of quantiles to be computed. It corresponds to the number of landmarks used to discretize the cumulative distribution function. If n_quantiles is larger than the number of samples, n_quantiles is set to the number of samples as a larger number of quantiles does not give a better approximation of the cumulative distribution function estimator. Default Value 1000 |
opts.output_distribution? | "uniform" | "normal" | Marginal distribution for the transformed data. The choices are ‘uniform’ (default) or ‘normal’. Default Value 'uniform' |
opts.random_state? | number | Determines random number generation for subsampling and smoothing noise. Please see subsample for more details. Pass an int for reproducible results across multiple function calls. See Glossary. |
opts.subsample? | number | Maximum number of samples used to estimate the quantiles for computational efficiency. Note that the subsampling procedure may differ for value-identical sparse and dense matrices. Default Value 10 |
Returns
Defined in: generated/preprocessing/QuantileTransformer.ts:29 (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/preprocessing/QuantileTransformer.ts:137 (opens in a new tab)
fit()
Compute the quantiles used for transforming.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The data used to scale along the features axis. If a sparse matrix is provided, it will be converted into a sparse csc\_matrix. Additionally, the sparse matrix needs to be nonnegative if ignore\_implicit\_zeros is false. |
opts.y? | any | Ignored. |
Returns
Promise<any>
Defined in: generated/preprocessing/QuantileTransformer.ts:154 (opens in a new tab)
fit_transform()
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit\_params and returns a transformed version of X.
Signature
fit_transform(opts: object): Promise<any[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Input samples. |
opts.fit_params? | any | Additional fit parameters. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns
Promise<any[]>
Defined in: generated/preprocessing/QuantileTransformer.ts:196 (opens in a new tab)
get_feature_names_out()
Get output feature names for transformation.
Signature
get_feature_names_out(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Input features. |
Returns
Promise<any>
Defined in: generated/preprocessing/QuantileTransformer.ts:247 (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/preprocessing/QuantileTransformer.ts:287 (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/preprocessing/QuantileTransformer.ts:87 (opens in a new tab)
inverse_transform()
Back-projection to the original space.
Signature
inverse_transform(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The data used to scale along the features axis. If a sparse matrix is provided, it will be converted into a sparse csc\_matrix. Additionally, the sparse matrix needs to be nonnegative if ignore\_implicit\_zeros is false. |
Returns
Promise<any>
Defined in: generated/preprocessing/QuantileTransformer.ts:325 (opens in a new tab)
set_output()
Set output container.
See Introducing the set_output API for an example on how to use the API.
Signature
set_output(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.transform? | "default" | "pandas" | Configure output of transform and fit\_transform. |
Returns
Promise<any>
Defined in: generated/preprocessing/QuantileTransformer.ts:365 (opens in a new tab)
transform()
Feature-wise transformation of the data.
Signature
transform(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The data used to scale along the features axis. If a sparse matrix is provided, it will be converted into a sparse csc\_matrix. Additionally, the sparse matrix needs to be nonnegative if ignore\_implicit\_zeros is false. |
Returns
Promise<ArrayLike>
Defined in: generated/preprocessing/QuantileTransformer.ts:402 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/preprocessing/QuantileTransformer.ts:27 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/preprocessing/QuantileTransformer.ts:26 (opens in a new tab)
_py
PythonBridge
Defined in: generated/preprocessing/QuantileTransformer.ts:25 (opens in a new tab)
id
string
Defined in: generated/preprocessing/QuantileTransformer.ts:22 (opens in a new tab)
opts
any
Defined in: generated/preprocessing/QuantileTransformer.ts:23 (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/preprocessing/QuantileTransformer.ts:545 (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/preprocessing/QuantileTransformer.ts:518 (opens in a new tab)
n_quantiles_
The actual number of quantiles used to discretize the cumulative distribution function.
Signature
n_quantiles_(): Promise<number>;Returns
Promise<number>
Defined in: generated/preprocessing/QuantileTransformer.ts:437 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/preprocessing/QuantileTransformer.ts:74 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/preprocessing/QuantileTransformer.ts:78 (opens in a new tab)
quantiles_
The values corresponding the quantiles of reference.
Signature
quantiles_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/preprocessing/QuantileTransformer.ts:464 (opens in a new tab)
references_
Quantiles of references.
Signature
references_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/preprocessing/QuantileTransformer.ts:491 (opens in a new tab)