IncrementalPCA
Incremental principal components analysis (IPCA).
Linear dimensionality reduction using Singular Value Decomposition of the data, keeping only the most significant singular vectors to project the data to a lower dimensional space. The input data is centered but not scaled for each feature before applying the SVD.
Depending on the size of the input data, this algorithm can be much more memory efficient than a PCA, and allows sparse input.
This algorithm has constant memory complexity, on the order of batch\_size \* n\_features, enabling use of np.memmap files without loading the entire file into memory. For sparse matrices, the input is converted to dense in batches (in order to be able to subtract the mean) which avoids storing the entire dense matrix at any one time.
The computational overhead of each SVD is O(batch\_size \* n\_features \*\* 2), but only 2 * batch_size samples remain in memory at a time. There will be n\_samples / batch\_size SVD computations to get the principal components, versus 1 large SVD of complexity O(n\_samples \* n\_features \*\* 2) for PCA.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new IncrementalPCA(opts?: object): IncrementalPCA;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.batch_size? | number | The number of samples to use for each batch. Only used when calling fit. If batch\_size is undefined, then batch\_size is inferred from the data and set to 5 \* n\_features, to provide a balance between approximation accuracy and memory consumption. |
opts.copy? | boolean | If false, X will be overwritten. copy=False can be used to save memory but is unsafe for general use. Default Value true |
opts.n_components? | number | Number of components to keep. If n\_components is undefined, then n\_components is set to min(n\_samples, n\_features). |
opts.whiten? | boolean | When true (false by default) the components\_ vectors are divided by n\_samples times components\_ to ensure uncorrelated outputs with unit component-wise variances. Whitening will remove some information from the transformed signal (the relative variance scales of the components) but can sometimes improve the predictive accuracy of the downstream estimators by making data respect some hard-wired assumptions. Default Value false |
Returns
Defined in: generated/decomposition/IncrementalPCA.ts:31 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/decomposition/IncrementalPCA.ts:29 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/decomposition/IncrementalPCA.ts:28 (opens in a new tab)
_py
PythonBridge
Defined in: generated/decomposition/IncrementalPCA.ts:27 (opens in a new tab)
id
string
Defined in: generated/decomposition/IncrementalPCA.ts:24 (opens in a new tab)
opts
any
Defined in: generated/decomposition/IncrementalPCA.ts:25 (opens in a new tab)
Accessors
batch_size_
Inferred batch size from batch\_size.
Signature
batch_size_(): Promise<number>;Returns
Promise<number>
Defined in: generated/decomposition/IncrementalPCA.ts:704 (opens in a new tab)
components_
Principal axes in feature space, representing the directions of maximum variance in the data. Equivalently, the right singular vectors of the centered input data, parallel to its eigenvectors. The components are sorted by decreasing explained\_variance\_.
Signature
components_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/decomposition/IncrementalPCA.ts:483 (opens in a new tab)
explained_variance_
Variance explained by each of the selected components.
Signature
explained_variance_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/decomposition/IncrementalPCA.ts:508 (opens in a new tab)
explained_variance_ratio_
Percentage of variance explained by each of the selected components. If all components are stored, the sum of explained variances is equal to 1.0.
Signature
explained_variance_ratio_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/decomposition/IncrementalPCA.ts:533 (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/IncrementalPCA.ts:754 (opens in a new tab)
mean_
Per-feature empirical mean, aggregate over calls to partial\_fit.
Signature
mean_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/decomposition/IncrementalPCA.ts:583 (opens in a new tab)
n_components_
The estimated number of components. Relevant when n\_components=None.
Signature
n_components_(): Promise<number>;Returns
Promise<number>
Defined in: generated/decomposition/IncrementalPCA.ts:654 (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/IncrementalPCA.ts:729 (opens in a new tab)
n_samples_seen_
The number of samples processed by the estimator. Will be reset on new calls to fit, but increments across partial\_fit calls.
Signature
n_samples_seen_(): Promise<number>;Returns
Promise<number>
Defined in: generated/decomposition/IncrementalPCA.ts:679 (opens in a new tab)
noise_variance_
The estimated noise covariance following the Probabilistic PCA model from Tipping and Bishop 1999. See “Pattern Recognition and Machine Learning” by C. Bishop, 12.2.1 p. 574 or http://www.miketipping.com/papers/met-mppca.pdf (opens in a new tab).
Signature
noise_variance_(): Promise<number>;Returns
Promise<number>
Defined in: generated/decomposition/IncrementalPCA.ts:629 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/decomposition/IncrementalPCA.ts:62 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/decomposition/IncrementalPCA.ts:66 (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/IncrementalPCA.ts:558 (opens in a new tab)
var_
Per-feature empirical variance, aggregate over calls to partial\_fit.
Signature
var_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/decomposition/IncrementalPCA.ts:606 (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/IncrementalPCA.ts:117 (opens in a new tab)
fit()
Fit the model with X, using minibatches of size batch_size.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training data, where n\_samples is the number of samples and n\_features is the number of features. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/decomposition/IncrementalPCA.ts:134 (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/decomposition/IncrementalPCA.ts:174 (opens in a new tab)
get_covariance()
Compute data covariance with the generative model.
cov \= components\_.T \* S\*\*2 \* components\_ + sigma2 \* eye(n\_features) where S**2 contains the explained variances, and sigma2 contains the noise variances.
Signature
get_covariance(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.cov? | any | Estimated covariance of data. |
Returns
Promise<any>
Defined in: generated/decomposition/IncrementalPCA.ts:223 (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/IncrementalPCA.ts:258 (opens in a new tab)
get_precision()
Compute data precision matrix with the generative model.
Equals the inverse of the covariance but computed with the matrix inversion lemma for efficiency.
Signature
get_precision(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.precision? | any | Estimated precision of data. |
Returns
Promise<any>
Defined in: generated/decomposition/IncrementalPCA.ts:296 (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/IncrementalPCA.ts:75 (opens in a new tab)
inverse_transform()
Transform data back to its original space.
In other words, return an input X\_original whose transform would be X.
Signature
inverse_transform(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | New data, where n\_samples is the number of samples and n\_components is the number of components. |
Returns
Promise<any>
Defined in: generated/decomposition/IncrementalPCA.ts:331 (opens in a new tab)
partial_fit()
Incremental fit with X. All of X is processed as a single batch.
Signature
partial_fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Training data, where n\_samples is the number of samples and n\_features is the number of features. |
opts.check_input? | boolean | Run check_array on X. Default Value true |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/decomposition/IncrementalPCA.ts:366 (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/IncrementalPCA.ts:415 (opens in a new tab)
transform()
Apply dimensionality reduction to X.
X is projected on the first principal components previously extracted from a training set, using minibatches of size batch_size if X is sparse.
Signature
transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | New data, where n\_samples is the number of samples and n\_features is the number of features. |
Returns
Promise<ArrayLike[]>
Defined in: generated/decomposition/IncrementalPCA.ts:450 (opens in a new tab)