How to do spectral clustering using Dask?
This recipe helps you do spectral clustering using Dask
Recipe Objective
How to do spectral clustering using Dask.
Spectral clustering scales the number of samples as per the model requirement, The Dask version uses an approximation to the affinity matrix, which reduces expensive computation.
#!pip install dask_ml --upgrade
#!pip install dask distributed --upgrade
Step 1- Importing Libraries
We will import the dataset make_circles and the clusters from dask_ml.
from sklearn.datasets import make_circles
from sklearn.utils import shuffle
import pandas as pd
from timeit import default_timer as tic
import dask_ml.cluster
Step 2- Spllting dataset.
We will split the dataset into x and y to feed into clustering algorithm.
x, y = make_circles(n_samples=10_000, noise=0.05, random_state=0, factor=0.2)
Step 3- Creating Clusters.
We will do the spectral clustering, with defining the clusters and n_components, then we will fit the model.
Ns = [500, 1000, 2500, 5000]
timings = []
for n in Ns:
t1 = tic()
dask_ml.cluster.SpectralClustering(n_clusters=2, n_components=100).fit(x)
timings.append(('dask-ml (approximate)', n, tic() - t1))
df = pd.DataFrame(timings, columns=['method', 'Samples', 'Fitting Time'])
df
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