Add documentation for LassoNetAutoEncoder
ElrondL opened this issue · comments
In the example mnist_ae.py the module LassoNetAutoEncoder is imported from lassonet, but this is not in the documentation?
Does LassoNet have an autoencoder implementation? If not, is it possible to create one by connecting two LassoNetRegressors?
@ilemhadri do you know how the autoencoders were implemented in the paper?
My best guess is that it was something along the lines of
from sklearn.datasets import fetch_california_housing
from sklearn.preprocessing import StandardScaler
from lassonet import LassoNetRegressor
X, _ = fetch_california_housing(return_X_y=True)
X = StandardScaler().fit_transform(X)
model = LassoNetRegressor(verbose=2)
path = model.path(X, X)My reference is #25
That works for me too. Is there any idea similar to a ‘latent space’ in LassoNetRegressor when used for unsupervised learning?
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Sent: 04 November 2023 07:04
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Subject: Re: [lasso-net/lassonet] Add documentation for LassoNetAutoEncoder (Issue #47)
As of Oct 2022, it seems this used to work
from sklearn.datasets import fetch_california_housing
from sklearn.preprocessing import StandardScaler
from lassonet import LassoNetRegressor
X, _ = fetch_california_housing(return_X_y=True)
X = StandardScaler().fit_transform(X)
model = LassoNetRegressor(verbose=2)
path = model.path(X, X)
My reference is #25<#25>
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I’m mainly looking for a way to output the feature space
Sent from Mail<https://go.microsoft.com/fwlink/?LinkId=550986> for Windows
From: ***@***.***>
Sent: 04 November 2023 07:04
To: ***@***.***>
Cc: ***@***.***>; ***@***.***>
Subject: Re: [lasso-net/lassonet] Add documentation for LassoNetAutoEncoder (Issue #47)
As of Oct 2022, it seems this used to work
from sklearn.datasets import fetch_california_housing
from sklearn.preprocessing import StandardScaler
from lassonet import LassoNetRegressor
X, _ = fetch_california_housing(return_X_y=True)
X = StandardScaler().fit_transform(X)
model = LassoNetRegressor(verbose=2)
path = model.path(X, X)
My reference is #25<#25>
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Reply to this email directly, view it on GitHub<#47 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/ALNNXLMYGX5XZNSMPCGVFC3YCZDQPAVCNFSM6AAAAAA6LNGJWCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTOOJTGQ2TENRUGQ>.
You are receiving this because you authored the thread.Message ID: ***@***.***>