How to do SVD with Dask?
This recipe helps you do SVD with Dask
Recipe Objective
How to do SVD with Dask?
SVD stands for Singular value Decomposition. This transformer performs linear dimensionality reduction by means of truncated singular value decomposition (SVD).
Step 1- Importing Libraries.
import dask.array as da
import dask
from dask_ml.decomposition import TruncatedSVD
import dask.array as da
Step 2- Instantiating SVD.
svd = TruncatedSVD(n_components=5, n_iter=3, random_state=42)
svd.fit(X)
Step 3- Printing explained variance ratio sum.
print(svd.explained_variance_ratio_.sum())
Additional Info.
Visualizing how this tranformer works.
X = da.random.random((100, 10), chunks=(50, 10)).persist()
x, y, z = da.linalg.svd(X)
dask.visualize(x, y, z)
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