lale.lib.autogen.factor_analysis module

class lale.lib.autogen.factor_analysis.FactorAnalysis(*, n_components=None, tol=0.01, copy=True, max_iter=1000, noise_variance_init=None, svd_method='randomized', iterated_power=3, random_state=0, rotation=None)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

• n_components (union type, not for optimizer, default None) –

  Dimensionality of latent space, the number of components of X that are obtained after transform

  • integer, >=2 for optimizer, <=’X/items/maxItems’, <=256 for optimizer, uniform distribution

  • or None

• tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, default 0.01) – Stopping tolerance for EM algorithm.

• copy (boolean, default True) – Whether to make a copy of X

• max_iter (integer, >=10 for optimizer, <=1000 for optimizer, uniform distribution, not for optimizer, default 1000) – Maximum number of iterations.

• noise_variance_init (None, not for optimizer, default None) – The initial guess of the noise variance for each feature

• svd_method (‘lapack’ or ‘randomized’, default ‘randomized’) –

  Which SVD method to use

  See also constraint-3, constraint-3.

• iterated_power (integer, >=3 for optimizer, <=4 for optimizer, uniform distribution, default 3) – Number of iterations for the power method

• random_state (union type, not for optimizer, default 0) –

  If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random

  • integer

  • or numpy.random.RandomState

  • or None

  See also constraint-3.

• rotation (‘varimax’, ‘quartimax’, or None, optional, not for optimizer, default None) – if not None, apply the indicated rotation. Currently, varimax and quartimax are implemented.

Notes

constraint-1 : any type

constraint-2 : any type

constraint-3 : union type

(‘random_state’ only used when svd_method equals ‘randomized’) From /utils/validation.py:None:check_random_state, Exception: raise ValueError( ‘%r cannot be used to seed a numpy.random.RandomState instance’ % seed)

• svd_method : ‘lapack’

• or svd_method : negated type of ‘randomized’

• or random_state : None

• or any type

• or any type

constraint-4 : negated type of ‘X/isSparse’

A sparse matrix was passed, but dense data is required. Use X.toarray() to convert to a dense numpy array.

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

• X (array of items : array of items : float) – Training data.

• y (any type) –

transform(X, y=None)

Transform the data.

Note: The transform method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (array of items : array of items : float) – Training data.

Returns

result – The latent variables of X.

Return type

array of items : array of items : float