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License: Other
Principal Component Analysis (PCA) for Missing and/or Noisy Data
License: Other
Hi, Can you please tell me how can I use for missing data imputation for this program ? It is written as
Missing data is simply the limit of weight=0.
But I am not getting where to set weight = 0. When I did
m0 = empca(noisy_data, weights = 0, niter=20)
it gives error as
File "empca.py", line 290, in empca
assert data.shape == weights.shape
Can you please help me ? I want to use your code for imputation problem. Thank you.
Hi,
I just started using empca and it works great. However in a few instances, I have the following Warning which seems to make the PCA stop:
empca.py:129: RuntimeWarning: invalid value encountered in double_scalars
self.eigvec[k, j] = x.dot(cw) / c.dot(cw)
Any advice on how to get rid of this message?
Thanks in advance.
I am trying to run with a large data set, ~200,000 eBOSS spectra, and stumbled upon an issue with memory.
What would be the best strategy to deal with that?
Is there an option float32
, or should I split the spectra I am looking into computing
in half according to lambdaRF and tape as best as I can after?
INFO: Starting EMPCA
iter R2 rchi2
Traceback (most recent call last):
File "<HOME>/redvsblue/bin//redvsblue_compute_PCA.py", line 205, in <module>
model = empca.empca(pcaflux, weights=pcaivar, niter=args.niter, nvec=args.nvec)
File "<HOME>/Programs/sbailey/empca/empca.py", line 307, in empca
model.solve_eigenvectors(smooth=smooth)
File "<HOME>/Programs/sbailey/empca/empca.py", line 142, in solve_eigenvectors
data -= np.outer(self.coeff[:,k], self.eigvec[k])
File "<HOME>/.local/lib/python3.6/site-packages/numpy/core/numeric.py", line 1203, in outer
return multiply(a.ravel()[:, newaxis], b.ravel()[newaxis, :], out)
MemoryError
I get the following warning when using weights, I bet it can simply be corrected:
<me>/Programs/sbailey/empca/empca.py:256: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.
To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.
x = np.linalg.lstsq(A, b)[0]
Here is a minimal example of code:
import scipy as sp
import empca
flux = sp.loadtxt('exemple_flux.txt')
weights = sp.loadtxt('exemple_weight.txt')
model = empca.empca(flux, niter=1, nvec=1, weights=weights)
As reported by Lingfeng Cheng at Cornell, input data cannot have NaN values even if the corresponding values are masked with weights=0. Normally masked data (weights=0) are effectively ignored via line 247:
b = A.T.dot( w*b )
But if any of the data in b
are NaN, this results in a NaN output, not a 0 output for that element even if w (=weights) is 0. This results in an error like:
ValueError: On entry to DGELSD parameter number 6 had an illegal value
At minimum, catch NaNs in the input and report as a meaningful error. Explore whether masked NaNs can be ignored without requiring rewriting or recopying the input array.
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