Class: FactorAnalysis
Factor Analysis (FA).
A simple linear generative model with Gaussian latent variables.
The observations are assumed to be caused by a linear transformation of lower dimensional latent factors and added Gaussian noise. Without loss of generality the factors are distributed according to a Gaussian with zero mean and unit covariance. The noise is also zero mean and has an arbitrary diagonal covariance matrix.
If we would restrict the model further, by assuming that the Gaussian noise is even isotropic (all diagonal entries are the same) we would obtain PCA.
FactorAnalysis performs a maximum likelihood estimate of the so-called loading matrix, the transformation of the latent variables to the observed ones, using SVD based approach.
Read more in the User Guide.
Constructors
new FactorAnalysis()
new FactorAnalysis(
opts?):FactorAnalysis
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.copy? | boolean | Whether to make a copy of X. If false, the input X gets overwritten during fitting. |
opts.iterated_power? | number | Number of iterations for the power method. 3 by default. Only used if svd_method equals ‘randomized’. |
opts.max_iter? | number | Maximum number of iterations. |
opts.n_components? | number | Dimensionality of latent space, the number of components of X that are obtained after transform. If undefined, n_components is set to the number of features. |
opts.noise_variance_init? | ArrayLike | The initial guess of the noise variance for each feature. If undefined, it defaults to np.ones(n_features). |
opts.random_state? | number | Only used when svd_method equals ‘randomized’. Pass an int for reproducible results across multiple function calls. See Glossary. |
opts.rotation? | "varimax" | "quartimax" | If not undefined, apply the indicated rotation. Currently, varimax and quartimax are implemented. See “The varimax criterion for analytic rotation in factor analysis” H. F. Kaiser, 1958. |
opts.svd_method? | "randomized" | "lapack" | Which SVD method to use. If ‘lapack’ use standard SVD from scipy.linalg, if ‘randomized’ use fast randomized_svd function. Defaults to ‘randomized’. For most applications ‘randomized’ will be sufficiently precise while providing significant speed gains. Accuracy can also be improved by setting higher values for iterated_power. If this is not sufficient, for maximum precision you should choose ‘lapack’. |
opts.tol? | number | Stopping tolerance for log-likelihood increase. |
Returns FactorAnalysis
Defined in generated/decomposition/FactorAnalysis.ts:31
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/decomposition/FactorAnalysis.ts:29 |
_isInitialized | boolean | false | generated/decomposition/FactorAnalysis.ts:28 |
_py | PythonBridge | undefined | generated/decomposition/FactorAnalysis.ts:27 |
id | string | undefined | generated/decomposition/FactorAnalysis.ts:24 |
opts | any | undefined | generated/decomposition/FactorAnalysis.ts:25 |
Accessors
components_
Get Signature
get components_():
Promise<ArrayLike[]>
Components with maximum variance.
Returns Promise<ArrayLike[]>
Defined in generated/decomposition/FactorAnalysis.ts:518
feature_names_in_
Get Signature
get feature_names_in_():
Promise<ArrayLike>
Names of features seen during fit. Defined only when X has feature names that are all strings.
Returns Promise<ArrayLike>
Defined in generated/decomposition/FactorAnalysis.ts:666
loglike_
Get Signature
get loglike_():
Promise<any[]>
The log likelihood at each iteration.
Returns Promise<any[]>
Defined in generated/decomposition/FactorAnalysis.ts:543
mean_
Get Signature
get mean_():
Promise<ArrayLike>
Per-feature empirical mean, estimated from the training set.
Returns Promise<ArrayLike>
Defined in generated/decomposition/FactorAnalysis.ts:618
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/decomposition/FactorAnalysis.ts:641
n_iter_
Get Signature
get n_iter_():
Promise<number>
Number of iterations run.
Returns Promise<number>
Defined in generated/decomposition/FactorAnalysis.ts:593
noise_variance_
Get Signature
get noise_variance_():
Promise<ArrayLike>
The estimated noise variance for each feature.
Returns Promise<ArrayLike>
Defined in generated/decomposition/FactorAnalysis.ts:568
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/decomposition/FactorAnalysis.ts:93
Methods
dispose()
dispose():
Promise<void>
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Returns Promise<void>
Defined in generated/decomposition/FactorAnalysis.ts:145
fit()
fit(
opts):Promise<any>
Fit the FactorAnalysis model to X using SVD based approach.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Training data. |
opts.y? | any | Ignored parameter. |
Returns Promise<any>
Defined in generated/decomposition/FactorAnalysis.ts:162
fit_transform()
fit_transform(
opts):Promise<any[]>
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.fit_params? | any | Additional fit parameters. |
opts.X? | ArrayLike[] | Input samples. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns Promise<any[]>
Defined in generated/decomposition/FactorAnalysis.ts:201
get_covariance()
get_covariance(
opts):Promise<any>
Compute data covariance with the FactorAnalysis model.
cov \= components_.T \* components_ + diag(noise_variance)
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.cov? | ArrayLike[] | Estimated covariance of data. |
Returns Promise<any>
Defined in generated/decomposition/FactorAnalysis.ts:245
get_feature_names_out()
get_feature_names_out(
opts):Promise<any>
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: \["class_name0", "class_name1", "class_name2"\].
Parameters
| Parameter | 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/FactorAnalysis.ts:279
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns Promise<any>
Defined in generated/decomposition/FactorAnalysis.ts:315
get_precision()
get_precision(
opts):Promise<any>
Compute data precision matrix with the FactorAnalysis model.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.precision? | ArrayLike[] | Estimated precision of data. |
Returns Promise<any>
Defined in generated/decomposition/FactorAnalysis.ts:349
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/decomposition/FactorAnalysis.ts:106
score()
score(
opts):Promise<number>
Compute the average log-likelihood of the samples.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The data. |
opts.y? | any | Ignored parameter. |
Returns Promise<number>
Defined in generated/decomposition/FactorAnalysis.ts:381
score_samples()
score_samples(
opts):Promise<ArrayLike>
Compute the log-likelihood of each sample.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The data. |
Returns Promise<ArrayLike>
Defined in generated/decomposition/FactorAnalysis.ts:418
set_output()
set_output(
opts):Promise<any>
Set output container.
See Introducing the set_output API for an example on how to use the API.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.transform? | "default" | "pandas" | "polars" | Configure output of transform and fit_transform. |
Returns Promise<any>
Defined in generated/decomposition/FactorAnalysis.ts:452
transform()
transform(
opts):Promise<ArrayLike[]>
Apply dimensionality reduction to X using the model.
Compute the expected mean of the latent variables. See Barber, 21.2.33 (or Bishop, 12.66).
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Training data. |
Returns Promise<ArrayLike[]>