SparseRandomProjection
Reduce dimensionality through sparse random projection.
Sparse random matrix is an alternative to dense random projection matrix that guarantees similar embedding quality while being much more memory efficient and allowing faster computation of the projected data.
If we note s \= 1 / density the components of the random matrix are drawn from:
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new SparseRandomProjection(opts?: object): SparseRandomProjection;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.compute_inverse_components? | boolean | Learn the inverse transform by computing the pseudo-inverse of the components during fit. Note that the pseudo-inverse is always a dense array, even if the training data was sparse. This means that it might be necessary to call inverse\_transform on a small batch of samples at a time to avoid exhausting the available memory on the host. Moreover, computing the pseudo-inverse does not scale well to large matrices. Default Value false |
opts.dense_output? | boolean | If true, ensure that the output of the random projection is a dense numpy array even if the input and random projection matrix are both sparse. In practice, if the number of components is small the number of zero components in the projected data will be very small and it will be more CPU and memory efficient to use a dense representation. If false, the projected data uses a sparse representation if the input is sparse. Default Value false |
opts.density? | number | "auto" | Ratio in the range (0, 1] of non-zero component in the random projection matrix. If density = ‘auto’, the value is set to the minimum density as recommended by Ping Li et al.: 1 / sqrt(n_features). Use density = 1 / 3.0 if you want to reproduce the results from Achlioptas, 2001. Default Value 'auto' |
opts.eps? | number | Parameter to control the quality of the embedding according to the Johnson-Lindenstrauss lemma when n_components is set to ‘auto’. This value should be strictly positive. Smaller values lead to better embedding and higher number of dimensions (n_components) in the target projection space. Default Value 0.1 |
opts.n_components? | number | "auto" | Dimensionality of the target projection space. n_components can be automatically adjusted according to the number of samples in the dataset and the bound given by the Johnson-Lindenstrauss lemma. In that case the quality of the embedding is controlled by the eps parameter. It should be noted that Johnson-Lindenstrauss lemma can yield very conservative estimated of the required number of components as it makes no assumption on the structure of the dataset. Default Value 'auto' |
opts.random_state? | number | Controls the pseudo random number generator used to generate the projection matrix at fit time. Pass an int for reproducible output across multiple function calls. See Glossary. |
Returns
Defined in: generated/random_projection/SparseRandomProjection.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/random_projection/SparseRandomProjection.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/random_projection/SparseRandomProjection.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/random_projection/SparseRandomProjection.ts:21 (opens in a new tab)
id
string
Defined in: generated/random_projection/SparseRandomProjection.ts:18 (opens in a new tab)
opts
any
Defined in: generated/random_projection/SparseRandomProjection.ts:19 (opens in a new tab)
Accessors
components_
Random matrix used for the projection. Sparse matrix will be of CSR format.
Signature
components_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/random_projection/SparseRandomProjection.ts:441 (opens in a new tab)
density_
Concrete density computed from when density = “auto”.
Signature
density_(): Promise<any>;Returns
Promise<any>
Defined in: generated/random_projection/SparseRandomProjection.ts:495 (opens in a new tab)
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/random_projection/SparseRandomProjection.ts:549 (opens in a new tab)
inverse_components_
Pseudo-inverse of the components, only computed if compute\_inverse\_components is true.
Signature
inverse_components_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/random_projection/SparseRandomProjection.ts:468 (opens in a new tab)
n_components_
Concrete number of components computed when n_components=”auto”.
Signature
n_components_(): Promise<number>;Returns
Promise<number>
Defined in: generated/random_projection/SparseRandomProjection.ts:414 (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/random_projection/SparseRandomProjection.ts:522 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/random_projection/SparseRandomProjection.ts:82 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/random_projection/SparseRandomProjection.ts:86 (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/random_projection/SparseRandomProjection.ts:145 (opens in a new tab)
fit()
Generate a sparse random projection matrix.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training set: only the shape is used to find optimal random matrix dimensions based on the theory referenced in the afore mentioned papers. |
opts.y? | any | Not used, present here for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/random_projection/SparseRandomProjection.ts:162 (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/random_projection/SparseRandomProjection.ts:204 (opens in a new tab)
get_feature_names_out()
Get output feature names for transformation.
The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: \["class\_name0", "class\_name1", "class\_name2"\].
Signature
get_feature_names_out(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Only used to validate feature names with the names seen in fit. |
Returns
Promise<any>
Defined in: generated/random_projection/SparseRandomProjection.ts:258 (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/random_projection/SparseRandomProjection.ts:95 (opens in a new tab)
inverse_transform()
Project data back to its original space.
Returns an array X_original whose transform would be X. Note that even if X is sparse, X_original is dense: this may use a lot of RAM.
If compute\_inverse\_components is false, the inverse of the components is computed during each call to inverse\_transform which can be costly.
Signature
inverse_transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Data to be transformed back. |
Returns
Promise<ArrayLike[]>
Defined in: generated/random_projection/SparseRandomProjection.ts:300 (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/random_projection/SparseRandomProjection.ts:340 (opens in a new tab)
transform()
Project the data by using matrix product with the random matrix.
Signature
transform(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input data to project into a smaller dimensional space. |
Returns
Promise<ArrayLike>
Defined in: generated/random_projection/SparseRandomProjection.ts:377 (opens in a new tab)