Source code for darfix.decomposition.nmf

__authors__ = ["J. Garriga"]
__license__ = "MIT"
__date__ = "06/04/2020"

import logging

import numpy
from .base import Base

_logger = logging.getLogger(__file__)


[docs]class NMF(Base): """ Non-Negative Matrix Factorization. Find two non-negative matrices whose product approximates the non-negative matrix data. """ def _init_w(self): if self.W is None: Base._init_w(self) self.W = numpy.abs(self.W) def _init_h(self): if self.H is None: Base._init_h(self) self.H = numpy.abs(self.H) def _update_h(self): _logger.info("Updating H") V, W, H = self.data, self.W, self.H if self.indices is None: for column in range(0, self.H.shape[1], self._hstep): mult = numpy.matmul(W.T, V[:, column : column + self._hstep]) / ( numpy.matmul( numpy.matmul(W.T, W), H[:, column : column + self._hstep] ) + 10**-9 ) self.H[:, column : column + self._hstep] *= mult else: for column in range(0, self.H.shape[1], self._hstep): mult = numpy.matmul( W.T, V[self.indices, column : column + self._hstep] ) / ( numpy.matmul( numpy.matmul(W.T, W), H[:, column : column + self._hstep] ) + 10**-9 ) self.H[:, column : column + self._hstep] *= mult def _update_w(self): _logger.info("Updating W") V, W, H = self.data, self.W, self.H if self.indices is None: for row in range(0, len(self.W), self._vstep): mult = numpy.matmul(V[row : row + self._vstep], H.T) / ( numpy.matmul(numpy.matmul(W[row : row + self._vstep], H), H.T) + 10**-9 ) self.W[row : row + self._vstep] *= mult else: for row in range(0, len(self.W), self._vstep): indx = self.indices[row : row + self._vstep] mult = numpy.matmul(V[indx], H.T) / ( numpy.matmul(numpy.matmul(W[row : row + self._vstep], H), H.T) + 10**-9 ) self.W[row : row + self._vstep] *= mult
[docs] def fit_transform( self, H=None, W=None, max_iter=1000, compute_w=True, compute_h=True, vstep=100, hstep=1000, error_step=None, ): """ Find the two non-negative matrices (H, W) using Lee and Seung's multiplicative update rule (https://proceedings.neurips.cc/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf). The images are loaded from disk in chunks. :param H: If not None, used as initial guess for the solution. :type H: array_like, shape (n_components, n_features), optional :param W: If not None, used as initial guess for the solution. :type W: array_like, shape (n_samples, n_components) :param max_iter: Maximum number of iterations before timing out, defaults to 200 :type max_iter: int, optional :param compute_w: If False, W is not computed. :type compute_w: bool, optional :param compute_h: If False, H is not computes. :type compute_h: bool, optional :param vstep: vertical size of the chunks to take from data. When updating W, `vstep` images are retrieved from disk per iteration, defaults to 100. :type vstep: int, optional :param hstep: horizontal size of the chunks to take from fata. When updating H, `hstep` pixels are retrieved from disk per iteration, defaults to 1000. :type hstep: int, optional :param error_step: If None, error is not computed, else compute error for every `error_step` iterations. :type error_step: Union[None,int], optional """ self.H = H self.W = W self._vstep = vstep self._hstep = hstep _logger.info("Starting NMF algorithm") Base.fit_transform( self, max_iter=max_iter, error_step=error_step, compute_w=compute_w, compute_h=compute_h, norm="squared_frobenius", )