Source code for stlearn.embedding.fa

import numpy as np
import pandas as pd
from typing import Optional

from anndata import AnnData
from sklearn.decomposition import FactorAnalysis
from scipy.sparse import issparse

[docs]def run_fa( adata: AnnData, n_factors: int = 20, tol: float = 0.01, max_iter: int = 1000, svd_method: str = "randomized", iterated_power: int = 3, random_state: int = 2108, use_data: str = None, copy: bool = False, ) -> Optional[AnnData]: """\ 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 :class:`PPCA`. 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. Parameters ---------- adata Annotated data matrix. n_components Dimensionality of latent space, the number of components of ``X`` that are obtained after ``transform``. If None, n_components is set to the number of features. tol Stopping tolerance for log-likelihood increase. max_iter Maximum number of iterations. noise_variance_init The initial guess of the noise variance for each feature. If None, it defaults to np.ones(n_features) svd_method 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'. iterated_power Number of iterations for the power method. 3 by default. Only used if ``svd_method`` equals 'randomized' random_state 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`. Only used when ``svd_method`` equals 'randomized'. copy Return a copy instead of writing to adata. Returns ------- Depending on `copy`, returns or updates `adata` with the following fields. `X_fa` : :class:`numpy.ndarray` (`adata.obsm`) Factor analysis representation of data. """ if use_data is None: if issparse(adata.X): matrix = adata.X.toarray() else: matrix = adata.X else: matrix = adata.obsm[use_data].values fa = FactorAnalysis( n_components=n_factors, tol=tol, max_iter=max_iter, svd_method=svd_method, iterated_power=iterated_power, random_state=random_state, ) latent = fa.fit_transform(matrix) adata.obsm["X_fa"] = latent adata.uns["fa_params"] = { "params": { "n_factors": n_factors, "tol": tol, "max_iter": max_iter, "svd_method": svd_method, "iterated_power": iterated_power, "random_state": random_state, } } print('FA is done! Generated in adata.obsm["X_fa"]') return adata if copy else None