How to do PCA with Dask?
This recipe helps you do PCA with Dask
Recipe Objective
How to do PCA with Dask?
PCA stands for **principal component Analysis**. It is used to reduce the dimensionality of a model using SVD to project in the lower dimensional data.
This algorithm depends on the size of the input data, SVD can be much more memory efficient than a PCA, and it allows sparse input as well. This algorithm has constant memory complexity.
#!pip install dask_ml
#!pip install dask distributed --upgrade
Table of Contents
Step 1- Importing Libraries.
Importing PCA from dask_ml.decomposition along with other libraries.
import numpy as np
import dask.array as da
from dask_ml.decomposition import PCA
Step 2- Creating arrays.
We will create multi dimensional array.
x = np.array([[1, -6], [2, -5], [3, -4], [4, -3], [5, -2], [6, -1]])
X = da.from_array(x, chunks=x.shape)
Step 3- Applying PCA to the arrays.
We will reduce the features by applying PCA to the arrays.
pca = PCA(n_components=2)
pca.fit(X)
Step 4- Printing explained variance ratio.
We will print the explained variance ratio to better understand the model working.
print(pca.explained_variance_ratio_)
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