TruncatedSVD
Dimensionality reduction using truncated SVD (aka LSA).
This transformer performs linear dimensionality reduction by means of truncated singular value decomposition (SVD). Contrary to PCA, this estimator does not center the data before computing the singular value decomposition. This means it can work with sparse matrices efficiently.
In particular, truncated SVD works on term count/tf-idf matrices as returned by the vectorizers in sklearn.feature\_extraction.text. In that context, it is known as latent semantic analysis (LSA).
This estimator supports two algorithms: a fast randomized SVD solver, and a “naive” algorithm that uses ARPACK as an eigensolver on X \* X.T or X.T \* X, whichever is more efficient.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new TruncatedSVD(opts?: object): TruncatedSVD;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.algorithm? | "randomized" | "arpack" | SVD solver to use. Either “arpack” for the ARPACK wrapper in SciPy (scipy.sparse.linalg.svds), or “randomized” for the randomized algorithm due to Halko (2009). Default Value 'randomized' |
opts.n_components? | number | Desired dimensionality of output data. If algorithm=’arpack’, must be strictly less than the number of features. If algorithm=’randomized’, must be less than or equal to the number of features. The default value is useful for visualisation. For LSA, a value of 100 is recommended. Default Value 2 |
opts.n_iter? | number | Number of iterations for randomized SVD solver. Not used by ARPACK. The default is larger than the default in randomized\_svd to handle sparse matrices that may have large slowly decaying spectrum. Default Value 5 |
opts.n_oversamples? | number | Number of oversamples for randomized SVD solver. Not used by ARPACK. See randomized\_svd for a complete description. Default Value 10 |
opts.power_iteration_normalizer? | "auto" | "QR" | "LU" | "none" | Power iteration normalizer for randomized SVD solver. Not used by ARPACK. See randomized\_svd for more details. Default Value 'auto' |
opts.random_state? | number | Used during randomized svd. Pass an int for reproducible results across multiple function calls. See Glossary. |
opts.tol? | number | Tolerance for ARPACK. 0 means machine precision. Ignored by randomized SVD solver. Default Value 0 |
Returns
Defined in: generated/decomposition/TruncatedSVD.ts:29 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/decomposition/TruncatedSVD.ts:27 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/decomposition/TruncatedSVD.ts:26 (opens in a new tab)
_py
PythonBridge
Defined in: generated/decomposition/TruncatedSVD.ts:25 (opens in a new tab)
id
string
Defined in: generated/decomposition/TruncatedSVD.ts:22 (opens in a new tab)
opts
any
Defined in: generated/decomposition/TruncatedSVD.ts:23 (opens in a new tab)
Accessors
components_
The right singular vectors of the input data.
Signature
components_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/decomposition/TruncatedSVD.ts:378 (opens in a new tab)
explained_variance_
The variance of the training samples transformed by a projection to each component.
Signature
explained_variance_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/decomposition/TruncatedSVD.ts:403 (opens in a new tab)
explained_variance_ratio_
Percentage of variance explained by each of the selected components.
Signature
explained_variance_ratio_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/decomposition/TruncatedSVD.ts:428 (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/decomposition/TruncatedSVD.ts:503 (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/decomposition/TruncatedSVD.ts:478 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/decomposition/TruncatedSVD.ts:81 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/decomposition/TruncatedSVD.ts:85 (opens in a new tab)
singular_values_
The singular values corresponding to each of the selected components. The singular values are equal to the 2-norms of the n\_components variables in the lower-dimensional space.
Signature
singular_values_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/decomposition/TruncatedSVD.ts:453 (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/decomposition/TruncatedSVD.ts:142 (opens in a new tab)
fit()
Fit model on training data X.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training data. |
opts.y? | any | Not used, present here for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/decomposition/TruncatedSVD.ts:159 (opens in a new tab)
fit_transform()
Fit model to X and perform dimensionality reduction on X.
Signature
fit_transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training data. |
opts.y? | any | Not used, present here for API consistency by convention. |
Returns
Promise<ArrayLike[]>
Defined in: generated/decomposition/TruncatedSVD.ts:197 (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/decomposition/TruncatedSVD.ts:237 (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/decomposition/TruncatedSVD.ts:94 (opens in a new tab)
inverse_transform()
Transform X back to its original space.
Returns an array X_original whose transform would be X.
Signature
inverse_transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | New data. |
Returns
Promise<ArrayLike[]>
Defined in: generated/decomposition/TruncatedSVD.ts:275 (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/decomposition/TruncatedSVD.ts:312 (opens in a new tab)
transform()
Perform dimensionality reduction on X.
Signature
transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | New data. |
Returns
Promise<ArrayLike[]>
Defined in: generated/decomposition/TruncatedSVD.ts:345 (opens in a new tab)