Overview
NumPy ConvergenceWarning: Number of distinct clusters (X) found smaller than n_clusters is a common warning message that arises when using clustering algorithms such as K-Means in Python’s Sci-kit Learn library. This tutorial explores possible reasons for the occurrence of this warning and presents varied solutions to address it.
Understanding The Warning
The warning signals that the algorithm was configured to find a certain number, say ‘Y’, of clusters, but was only able to identify ‘X’ unique clusters in the dataset. It typically arises when the mechanism for initializing cluster centers or the dataset itself does not support the desired number of clusters.
Let’s Fix It
Inspect Dataset Characteristics
Prior to tweaking your model, it’s important to understand your data.
- Step 1: Utilize descriptive statistics and visualizations to locate potential clustering structures or lack thereof.
- Step 2: Evaluate whether the number of clusters you wish to identify is reasonable given the data’s characteristics.
# Example of data visualization using matplotlib
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import make_blobs
# Create a sample dataset
X, y = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)
plt.scatter(X[:, 0], X[:, 1])
plt.show()
Notes:Visualization can help you adjust your expectations for the number of clusters.
Reduce ‘n_clusters’ Parameter
To address the warning, one straightforward approach is to set the ‘n_clusters’ to a value that does not exceed the number of distinct clusters actually present in the dataset.
Lower the ‘n_clusters’ value in the K-Means configuration to match the number of identifiable clusters. Below is an example:
# Example of adjusting n_clusters
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=3, random_state=0)
kmeans.fit(X) # Assuming 'X' is your dataset
print("Number of clusters found:", len(set(kmeans.labels_)))
Notes:While this solution is the most direct, it might not be suitable if a specific number of clusters is necessary from a domain perspective.
Change Initialization Method
Different initialization techniques can lead to varied clustering outcomes.
- Step 1: Change the ‘init’ parameter in K-Means to ‘k-means++’ instead of ‘random’ or vice versa.
- Step 2: Increase the ‘n_init’ parameter value to ensure variability in initial seeding.
# Example code to change initialization method
kmeans = KMeans(n_clusters=4, init='k-means++', n_init=10, random_state=0)
kmeans.fit(X)
print("Distinct clusters found:", len(set(kmeans.labels_)))
Notes:Using ‘k-means++’ can help in finding a more robust initial seed which may lead to identifying the desired number of clusters.
Rescale Features
Feature scaling can significantly affect the outcome of clustering algorithms.
Scale the features using StandardScaler or MinMaxScaler from sklearn.preprocessing, as shown in the example below:
# Example code for feature scaling
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
kmeans = KMeans(n_clusters=4, random_state=0)
kmeans.fit(X_scaled)
print("Distinct clusters found:", len(set(kmeans.labels_)))
Notes:Scaling can bring out natural clusters that may not have been evident in data with unscaled features, but sometimes the real-world interpretability of clusters becomes harder.